GPT-4 Can Reason Over Concepts and Solve Complex Problems. arXiv preprint
arXiv:2303.01101.
[7] Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020).
Language models are few-shot learners. arXiv preprint arXiv:2005.14165.
[8] Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... & Berner, C. (2021).
On the opportunities and risks of
Breaking down the AI magic of ChatGPT: A technologist's lens to its powerful ...rahul_net
ChatGPT has taken the world of natural language processing by storm, and as an experienced AI practitioner, enterprise architect, and technologist with over two decades of experience, I'm excited to share my insights on how this innovative powerhouse is designed from an AI components perspective. In this post, I'll provide a fresh take on the key components that make ChatGPT a powerful conversational AI tool, including its use of the Transformer architecture, pre-training on large amounts of text data, and fine-tuning with human feedback. With ChatGPT's massive success, there's no doubt that it's changing the way we think about language and conversation. So, whether you're a seasoned pro or new to the world of AI, my post will provide a valuable perspective on this fascinating technology. Check out my slides to learn more!
Sentence Validation by Statistical Language Modeling and Semantic RelationsEditor IJCATR
This paper deals with Sentence Validation - a sub-field of Natural Language Processing. It finds various applications in
different areas as it deals with understanding the natural language (English in most cases) and manipulating it. So the effort is on
understanding and extracting important information delivered to the computer and make possible efficient human computer
interaction. Sentence Validation is approached in two ways - by Statistical approach and Semantic approach. In both approaches
database is trained with the help of sample sentences of Brown corpus of NLTK. The statistical approach uses trigram technique based
on N-gram Markov Model and modified Kneser-Ney Smoothing to handle zero probabilities. As another testing on statistical basis,
tagging and chunking of the sentences having named entities is carried out using pre-defined grammar rules and semantic tree parsing,
and chunked off sentences are fed into another database, upon which testing is carried out. Finally, semantic analysis is carried out by
extracting entity relation pairs which are then tested. After the results of all three approaches is compiled, graphs are plotted and
variations are studied. Hence, a comparison of three different models is calculated and formulated. Graphs pertaining to the
probabilities of the three approaches are plotted, which clearly demarcate them and throw light on the findings of the project.
This document discusses n-gram language models. It provides an introduction to language models and their role in applications like speech recognition. Simple n-gram models are described that estimate word probabilities based on prior context. Parameter estimation and smoothing techniques are covered to address data sparsity issues from rare word combinations. Evaluation of language models on held-out test data is also mentioned.
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...kevig
This study investigates the effectiveness of Knowledge Named Entity Recognition in Online Judges (OJs). OJs are lacking in the classification of topics and limited to the IDs only. Therefore a lot of time is consumed in finding programming problems more specifically in knowledge entities.A Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Fields (CRF) model is applied for the recognition of knowledge named entities existing in the solution reports.For the test run, more than 2000 solution reports are crawled from the Online Judges and processed for the model output. The stability of the model is also assessed with the higher F1 value. The results obtained through the proposed BiLSTM-CRF model are more effectual (F1: 98.96%) and efficient in lead-time.
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...ijnlc
This study investigates the effectiveness of Knowledge Named Entity Recognition in Online Judges (OJs). OJs are lacking in the classification of topics and limited to the IDs only. Therefore a lot of time is consumed in finding programming problems more specifically in knowledge entities.A Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Fields (CRF) model is applied for the recognition of knowledge named entities existing in the solution reports.For the test run, more than 2000 solution reports are crawled from the Online Judges and processed for the model output. The stability of the model is
also assessed with the higher F1 value. The results obtained through the proposed BiLSTM-CRF model are more effectual (F1: 98.96%) and efficient in lead-time.
This document discusses machine learning algorithms and their suitability for parallelization using MapReduce. It begins by introducing machine learning and different types of algorithms, including supervised, unsupervised, and reinforcement learning. It then discusses how several common machine learning algorithms can be expressed using the MapReduce framework. Single-pass algorithms like language modeling and naive Bayes classification are well-suited since they involve extracting statistics from each data point. Iterative algorithms can also be parallelized by chaining multiple MapReduce jobs, though the map tasks may need access to global parameters. Overall, the document analyzes how different machine learning algorithms map to the data processing patterns of MapReduce.
Evaluation of Medium-Sized Language Models in German and English Languagekevig
Large language models (LLMs) have garnered significant attention, but the definition of “large” lacks clarity. This paper focuses on medium-sized language models (MLMs), defined as having at least six billion parameters but less than 100 billion. The study evaluates MLMs regarding zero-shot generative question answering, which requires models to provide elaborate answers without external document retrieval. The paper introduces an own test dataset and presents results from human evaluation. Results show that combining the best answers from different MLMs yielded an overall correct answer rate of 82.7% which is better than the 60.9% of ChatGPT. The best MLM achieved 71.8% and has 33B parameters, which highlights the importance of using appropriate training data for fine-tuning rather than solely relying on the number of parameters. More fine-grained feedback should be used to further improve the quality of answers. The open source community is quickly closing the gap to the best commercial models.
EVALUATION OF MEDIUM-SIZED LANGUAGE MODELS IN GERMAN AND ENGLISH LANGUAGEkevig
Large language models (LLMs) have garnered significant attention, but the definition of “large” lacks
clarity. This paper focuses on medium-sized language models (MLMs), defined as having at least six
billion parameters but less than 100 billion. The study evaluates MLMs regarding zero-shot generative
question answering, which requires models to provide elaborate answers without external document
retrieval. The paper introduces an own test dataset and presents results from human evaluation. Results
show that combining the best answers from different MLMs yielded an overall correct answer rate of
82.7% which is better than the 60.9% of ChatGPT. The best MLM achieved 71.8% and has 33B
parameters, which highlights the importance of using appropriate training data for fine-tuning rather than
solely relying on the number of parameters. More fine-grained feedback should be used to further improve
the quality of answers. The open source community is quickly closing the gap to the best commercial
models.
Breaking down the AI magic of ChatGPT: A technologist's lens to its powerful ...rahul_net
ChatGPT has taken the world of natural language processing by storm, and as an experienced AI practitioner, enterprise architect, and technologist with over two decades of experience, I'm excited to share my insights on how this innovative powerhouse is designed from an AI components perspective. In this post, I'll provide a fresh take on the key components that make ChatGPT a powerful conversational AI tool, including its use of the Transformer architecture, pre-training on large amounts of text data, and fine-tuning with human feedback. With ChatGPT's massive success, there's no doubt that it's changing the way we think about language and conversation. So, whether you're a seasoned pro or new to the world of AI, my post will provide a valuable perspective on this fascinating technology. Check out my slides to learn more!
Sentence Validation by Statistical Language Modeling and Semantic RelationsEditor IJCATR
This paper deals with Sentence Validation - a sub-field of Natural Language Processing. It finds various applications in
different areas as it deals with understanding the natural language (English in most cases) and manipulating it. So the effort is on
understanding and extracting important information delivered to the computer and make possible efficient human computer
interaction. Sentence Validation is approached in two ways - by Statistical approach and Semantic approach. In both approaches
database is trained with the help of sample sentences of Brown corpus of NLTK. The statistical approach uses trigram technique based
on N-gram Markov Model and modified Kneser-Ney Smoothing to handle zero probabilities. As another testing on statistical basis,
tagging and chunking of the sentences having named entities is carried out using pre-defined grammar rules and semantic tree parsing,
and chunked off sentences are fed into another database, upon which testing is carried out. Finally, semantic analysis is carried out by
extracting entity relation pairs which are then tested. After the results of all three approaches is compiled, graphs are plotted and
variations are studied. Hence, a comparison of three different models is calculated and formulated. Graphs pertaining to the
probabilities of the three approaches are plotted, which clearly demarcate them and throw light on the findings of the project.
This document discusses n-gram language models. It provides an introduction to language models and their role in applications like speech recognition. Simple n-gram models are described that estimate word probabilities based on prior context. Parameter estimation and smoothing techniques are covered to address data sparsity issues from rare word combinations. Evaluation of language models on held-out test data is also mentioned.
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...kevig
This study investigates the effectiveness of Knowledge Named Entity Recognition in Online Judges (OJs). OJs are lacking in the classification of topics and limited to the IDs only. Therefore a lot of time is consumed in finding programming problems more specifically in knowledge entities.A Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Fields (CRF) model is applied for the recognition of knowledge named entities existing in the solution reports.For the test run, more than 2000 solution reports are crawled from the Online Judges and processed for the model output. The stability of the model is also assessed with the higher F1 value. The results obtained through the proposed BiLSTM-CRF model are more effectual (F1: 98.96%) and efficient in lead-time.
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...ijnlc
This study investigates the effectiveness of Knowledge Named Entity Recognition in Online Judges (OJs). OJs are lacking in the classification of topics and limited to the IDs only. Therefore a lot of time is consumed in finding programming problems more specifically in knowledge entities.A Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Fields (CRF) model is applied for the recognition of knowledge named entities existing in the solution reports.For the test run, more than 2000 solution reports are crawled from the Online Judges and processed for the model output. The stability of the model is
also assessed with the higher F1 value. The results obtained through the proposed BiLSTM-CRF model are more effectual (F1: 98.96%) and efficient in lead-time.
This document discusses machine learning algorithms and their suitability for parallelization using MapReduce. It begins by introducing machine learning and different types of algorithms, including supervised, unsupervised, and reinforcement learning. It then discusses how several common machine learning algorithms can be expressed using the MapReduce framework. Single-pass algorithms like language modeling and naive Bayes classification are well-suited since they involve extracting statistics from each data point. Iterative algorithms can also be parallelized by chaining multiple MapReduce jobs, though the map tasks may need access to global parameters. Overall, the document analyzes how different machine learning algorithms map to the data processing patterns of MapReduce.
Evaluation of Medium-Sized Language Models in German and English Languagekevig
Large language models (LLMs) have garnered significant attention, but the definition of “large” lacks clarity. This paper focuses on medium-sized language models (MLMs), defined as having at least six billion parameters but less than 100 billion. The study evaluates MLMs regarding zero-shot generative question answering, which requires models to provide elaborate answers without external document retrieval. The paper introduces an own test dataset and presents results from human evaluation. Results show that combining the best answers from different MLMs yielded an overall correct answer rate of 82.7% which is better than the 60.9% of ChatGPT. The best MLM achieved 71.8% and has 33B parameters, which highlights the importance of using appropriate training data for fine-tuning rather than solely relying on the number of parameters. More fine-grained feedback should be used to further improve the quality of answers. The open source community is quickly closing the gap to the best commercial models.
EVALUATION OF MEDIUM-SIZED LANGUAGE MODELS IN GERMAN AND ENGLISH LANGUAGEkevig
Large language models (LLMs) have garnered significant attention, but the definition of “large” lacks
clarity. This paper focuses on medium-sized language models (MLMs), defined as having at least six
billion parameters but less than 100 billion. The study evaluates MLMs regarding zero-shot generative
question answering, which requires models to provide elaborate answers without external document
retrieval. The paper introduces an own test dataset and presents results from human evaluation. Results
show that combining the best answers from different MLMs yielded an overall correct answer rate of
82.7% which is better than the 60.9% of ChatGPT. The best MLM achieved 71.8% and has 33B
parameters, which highlights the importance of using appropriate training data for fine-tuning rather than
solely relying on the number of parameters. More fine-grained feedback should be used to further improve
the quality of answers. The open source community is quickly closing the gap to the best commercial
models.
The document proposes a new method called EosPurple that uses four components - Moore's Law, Markov models, secure models, and psychoacoustic methodologies - to realize Web services. It describes the design of EosPurple, which involves motivating the need for journaling file systems and confirming the improvement of evolutionary programming. The evaluation section outlines four experiments conducted to evaluate EosPurple and analyzes the results. The conclusion argues that EosPurple is a novel methodology for developing IPv4.
The document describes the Like2Vec recommender system model. It transforms sparse user-item rating matrices into a graph representation, and then uses the DeepWalk algorithm to learn embeddings of nodes in the graph. These embeddings are trained with the Skip-Gram language model on random walks generated through the graph. Like2Vec is evaluated on the Netflix dataset and is shown to outperform baselines in Recall-at-N, which directly measures the quality of top recommendations compared to RMSE which does not. Recall-at-N is argued to be a superior evaluation metric for recommender systems.
This document summarizes a research paper that proposes a new heuristic called PAUSE for investigating the producer-consumer problem in distributed systems. The paper motivates the need to study this problem, describes PAUSE's approach of using compact configurations and decentralized components, outlines its implementation in Lisp and Java, and presents experimental results showing PAUSE outperforms previous methods. Related work investigating similar challenges is also discussed.
FRACTAL ANALYSIS OF GOOD PROGRAMMING STYLEcscpconf
This paper studies a new, quantitative approach using fractal geometry to analyze basic tenets of good programming style. Experiments on C source of the GNU/Linux Core Utilities, a
collection of 114 programs or approximately 70,000 lines of code, show systematic changes in style are correlated with statistically significant changes in fractal dimension (P≤0.0009). The data further show positive but weak correlation between lines of code and fractal dimension (r=0.0878). These results suggest the fractal dimension is a reliable metric of changes that
affect good style, the knowledge of which may be useful for maintaining a code base.
This paper studies a new, quantitative approach using fractal geometry to analyse basic tenets
of good programming style. Experiments on C source of the GNU/Linux Core Utilities, a
collection of 114 programs or approximately 70,000 lines of code, show systematic changes in
style are correlated with statistically significant changes in fractal dimension (P≤0.0009). The
data further show positive but weak correlation between lines of code and fractal dimension
(r=0.0878). These results suggest the fractal dimension is a reliable metric of changes that
affect good style, the knowledge of which may be useful for maintaining a code base.
Analysis of the evolution of advanced transformer-based language models: Expe...IAESIJAI
This document analyzes the evolution of advanced transformer-based language models for opinion mining tasks. It provides background on several transformer models including BERT, GPT, ALBERT, RoBERTa, XLNet, DistilBERT, XLM-RoBERTa, BART, ConvBERT, Reformer, T5, ELECTRA, Longformer, and DeBERTa. The document compares these models based on their architecture, pre-training data, objectives, performance on tasks, and computational costs. It aims to study the behavior of these cutting-edge models on opinion mining and provide guidelines for researchers and engineers on model selection.
1. The document introduces Codex, a large language model fine-tuned on publicly available code from GitHub. It evaluates Codex's ability to generate Python code from docstrings on a new dataset of 164 programming problems called HumanEval.
2. Codex is able to solve 28.8% of problems in HumanEval with a single sample, compared to 0% for GPT-3 and 11.4% for GPT-J. Generating 100 samples per problem increases Codex's performance to 70.2%.
3. The document discusses limitations of existing code generation metrics and proposes evaluating functional correctness by checking if generated code passes unit tests. It also describes the sandbox used to safely execute generated code.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
The paper presents a neural probabilistic language model that overcomes the curse of dimensionality in probabilistic language modeling. It develops a neural network model with distributed word representations as parameters to learn the probability of sequences. The model learns representations for each word and the probability function as a function of these representations using a hidden and softmax layer. This allows the model to estimate probabilities of unseen sequences during training by taking advantage of longer contexts through continuous representations.
The large-scale cyberinformatics method to replication is defined not only by the analysis of local-area networks, but also by the structured need for the Internet. Here, we confirm the refinement of superpages, which embodies the unfortunate principles of operating systems. SHODE, our new methodology for secure methodologies, is the solution to all of these obstacles.
This paper discusses the capabilities and limitations of GPT-3 (0), a state-of-the-art language model, in the
context of text understanding. We begin by describing the architecture and training process of GPT-3, and
provide an overview of its impressive performance across a wide range of natural language processing
tasks, such as language translation, question-answering, and text completion. Throughout this research
project, a summarizing tool was also created to help us retrieve content from any types of document,
specifically IELTS (0) Reading Test data in this project. We also aimed to improve the accuracy of the
summarizing, as well as question-answering capabilities of GPT-3 (0) via long text
A comprehensive guide to prompt engineering.pdfJamieDornan2
Prompt engineering is the practice of designing text prompts to guide large language models towards generating specific, desired outputs without additional training. Carefully crafted prompts play a crucial role in extracting superior performance from language models and allowing them to excel at tasks like question answering, reasoning, and text generation. The effectiveness of prompt engineering is demonstrated in applications like ChatGPT, Google's Smart Reply, and AlphaGo. It is an important field that helps maximize models' potential while ensuring safety and performance for different domains.
Google's Pathways Language Model and Chain-of-ThoughtVaclav1
By Václav Košař (GLAMI): Pathways Language Model (PaLM) is a 540-billion parameter with architecture similar to GPT-3. This model, published April 4th, 2022, achieves breakthrough capabilities on language understanding and generation, reasoning, and coding tasks. For example for reasoning tasks, PaLM used chain-of-thought prompting, which applies simulated inner monologue to solve grade school level math questions. In this talk, we will discuss both general-public-accessible intuition of how knowledge and reasoning can be represented in computers as well as technical details of PaLM model architecture.
The document describes a comparative study of various machine learning and neural network models for detecting abusive language on Twitter. It finds that a bidirectional GRU network trained on word-level features, with a Latent Topic Clustering module, achieves the most accurate results with an F1 score of 0.805 for detecting abusive tweets. Additionally, it explores using context tweets as additional features and finds this improves some models' performance.
Automated Essay Scoring Using Efficient Transformer-Based Language ModelsNat Rice
This summary provides an overview of an academic paper that evaluates the performance of efficient transformer-based language models on an automated essay scoring dataset:
The paper explores using smaller, more efficient transformer models rather than larger ones like BERT for automated essay scoring (AES). It evaluates several efficient models - Albert, Reformer, Electra, and MobileBERT - on the ASAP AES dataset. By ensembling multiple efficient models, the paper achieves state-of-the-art results on the dataset using far fewer parameters than typical transformer models, challenging the idea that bigger models are always better for AES. The efficient models show potential for extending the maximum text length they can analyze and reducing computational requirements for AES.
This document provides an introduction to machine learning concepts. It begins with an overview of the book's organization and topics to be covered, including descriptive statistics, algebra, linear regression, classification, clustering, decision trees, and neural networks. It then discusses requisite skills like basic Python and software needed. The document provides definitions of machine learning and describes common problem types it can solve. It also outlines popular machine learning tools and frameworks.
May 2024 - Top10 Cited Articles in Natural Language Computingkevig
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
Multi-label learning by extended multi-tier stacked ensemble method with labe...IJECEIAES
Classification is one of the basic and most important operations that can be used in data science and machine learning applications. Multi-label classification is an extension of the multi-class problem where a set of class labels are associated with a particular instance at a time. In a multiclass problem, a single class label is associated with an instance at a time. However, there are many different stacked ensemble methods that have been proposed and because of the complexity associated with the multi-label problems, there is still a lot of scope for improving the prediction accuracy. In this paper, we are proposing the novel extended multi-tier stacked ensemble (EMSTE) method with label correlation by feature subset selection technique and then augmenting those feature subsets while constructing the intermediate dataset for improving the prediction accuracy in the generalization phase of the stacking. The performance effect of the proposed method has been compared with existing methods and showed that our proposed method outperforms the other methods.
The document proposes BergSump, a new framework for analyzing I/O automata. BergSump aims to confirm that superblocks and flip-flop gates are generally incompatible. It discusses related work on XML, wireless networks, and cryptography. The implementation section outlines version 5.9 of BergSump and plans to release the code under an open source license. The evaluation analyzes BergSump's performance and shows its median complexity is better than prior solutions. The conclusion argues that BergSump can successfully observe many sensor networks at once.
PhotoQR: A Novel ID Card with an Encoded Viewgerogepatton
There is an increasing interest in developing techniques to identify and assess data to allow an easy and
continuous access to resources, services or places that require thorough ID control. Usually, in order to
give access to these resources, different kinds of documents are mandatory. In order to avoid forgeries
without the need of extra credentials, a new system –named photoQR, is here proposed. This system is
based on a ID card having two objects: one person’s picture (pre-processed via blur and/or swirl
techniques) and one QR code containing embedded data related to the picture. The idea is that the picture
and the QR code can assess each other by a proper hash value in the QR. The QR without the picture
cannot be assessed and vice versa. An open source prototype of the photoQR system has been implemented
in Python and can be used both in offline and real-time environments, which effectively combines security
concepts and image processing algorithms to obtain data assessment.
12th International Conference of Artificial Intelligence and Fuzzy Logic (AI ...gerogepatton
12th International Conference of Artificial Intelligence and Fuzzy Logic (AI & FL 2024) provides a
forum for researchers who address this issue and to present their work in a peer-reviewed forum. Authors
are solicited to contribute to the conference by submitting articles that illustrate research results, projects,
surveying works and industrial experiences that describe significant advances in the following areas, but
are not limited to these topics only.
The document proposes a new method called EosPurple that uses four components - Moore's Law, Markov models, secure models, and psychoacoustic methodologies - to realize Web services. It describes the design of EosPurple, which involves motivating the need for journaling file systems and confirming the improvement of evolutionary programming. The evaluation section outlines four experiments conducted to evaluate EosPurple and analyzes the results. The conclusion argues that EosPurple is a novel methodology for developing IPv4.
The document describes the Like2Vec recommender system model. It transforms sparse user-item rating matrices into a graph representation, and then uses the DeepWalk algorithm to learn embeddings of nodes in the graph. These embeddings are trained with the Skip-Gram language model on random walks generated through the graph. Like2Vec is evaluated on the Netflix dataset and is shown to outperform baselines in Recall-at-N, which directly measures the quality of top recommendations compared to RMSE which does not. Recall-at-N is argued to be a superior evaluation metric for recommender systems.
This document summarizes a research paper that proposes a new heuristic called PAUSE for investigating the producer-consumer problem in distributed systems. The paper motivates the need to study this problem, describes PAUSE's approach of using compact configurations and decentralized components, outlines its implementation in Lisp and Java, and presents experimental results showing PAUSE outperforms previous methods. Related work investigating similar challenges is also discussed.
FRACTAL ANALYSIS OF GOOD PROGRAMMING STYLEcscpconf
This paper studies a new, quantitative approach using fractal geometry to analyze basic tenets of good programming style. Experiments on C source of the GNU/Linux Core Utilities, a
collection of 114 programs or approximately 70,000 lines of code, show systematic changes in style are correlated with statistically significant changes in fractal dimension (P≤0.0009). The data further show positive but weak correlation between lines of code and fractal dimension (r=0.0878). These results suggest the fractal dimension is a reliable metric of changes that
affect good style, the knowledge of which may be useful for maintaining a code base.
This paper studies a new, quantitative approach using fractal geometry to analyse basic tenets
of good programming style. Experiments on C source of the GNU/Linux Core Utilities, a
collection of 114 programs or approximately 70,000 lines of code, show systematic changes in
style are correlated with statistically significant changes in fractal dimension (P≤0.0009). The
data further show positive but weak correlation between lines of code and fractal dimension
(r=0.0878). These results suggest the fractal dimension is a reliable metric of changes that
affect good style, the knowledge of which may be useful for maintaining a code base.
Analysis of the evolution of advanced transformer-based language models: Expe...IAESIJAI
This document analyzes the evolution of advanced transformer-based language models for opinion mining tasks. It provides background on several transformer models including BERT, GPT, ALBERT, RoBERTa, XLNet, DistilBERT, XLM-RoBERTa, BART, ConvBERT, Reformer, T5, ELECTRA, Longformer, and DeBERTa. The document compares these models based on their architecture, pre-training data, objectives, performance on tasks, and computational costs. It aims to study the behavior of these cutting-edge models on opinion mining and provide guidelines for researchers and engineers on model selection.
1. The document introduces Codex, a large language model fine-tuned on publicly available code from GitHub. It evaluates Codex's ability to generate Python code from docstrings on a new dataset of 164 programming problems called HumanEval.
2. Codex is able to solve 28.8% of problems in HumanEval with a single sample, compared to 0% for GPT-3 and 11.4% for GPT-J. Generating 100 samples per problem increases Codex's performance to 70.2%.
3. The document discusses limitations of existing code generation metrics and proposes evaluating functional correctness by checking if generated code passes unit tests. It also describes the sandbox used to safely execute generated code.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
The paper presents a neural probabilistic language model that overcomes the curse of dimensionality in probabilistic language modeling. It develops a neural network model with distributed word representations as parameters to learn the probability of sequences. The model learns representations for each word and the probability function as a function of these representations using a hidden and softmax layer. This allows the model to estimate probabilities of unseen sequences during training by taking advantage of longer contexts through continuous representations.
The large-scale cyberinformatics method to replication is defined not only by the analysis of local-area networks, but also by the structured need for the Internet. Here, we confirm the refinement of superpages, which embodies the unfortunate principles of operating systems. SHODE, our new methodology for secure methodologies, is the solution to all of these obstacles.
This paper discusses the capabilities and limitations of GPT-3 (0), a state-of-the-art language model, in the
context of text understanding. We begin by describing the architecture and training process of GPT-3, and
provide an overview of its impressive performance across a wide range of natural language processing
tasks, such as language translation, question-answering, and text completion. Throughout this research
project, a summarizing tool was also created to help us retrieve content from any types of document,
specifically IELTS (0) Reading Test data in this project. We also aimed to improve the accuracy of the
summarizing, as well as question-answering capabilities of GPT-3 (0) via long text
A comprehensive guide to prompt engineering.pdfJamieDornan2
Prompt engineering is the practice of designing text prompts to guide large language models towards generating specific, desired outputs without additional training. Carefully crafted prompts play a crucial role in extracting superior performance from language models and allowing them to excel at tasks like question answering, reasoning, and text generation. The effectiveness of prompt engineering is demonstrated in applications like ChatGPT, Google's Smart Reply, and AlphaGo. It is an important field that helps maximize models' potential while ensuring safety and performance for different domains.
Google's Pathways Language Model and Chain-of-ThoughtVaclav1
By Václav Košař (GLAMI): Pathways Language Model (PaLM) is a 540-billion parameter with architecture similar to GPT-3. This model, published April 4th, 2022, achieves breakthrough capabilities on language understanding and generation, reasoning, and coding tasks. For example for reasoning tasks, PaLM used chain-of-thought prompting, which applies simulated inner monologue to solve grade school level math questions. In this talk, we will discuss both general-public-accessible intuition of how knowledge and reasoning can be represented in computers as well as technical details of PaLM model architecture.
The document describes a comparative study of various machine learning and neural network models for detecting abusive language on Twitter. It finds that a bidirectional GRU network trained on word-level features, with a Latent Topic Clustering module, achieves the most accurate results with an F1 score of 0.805 for detecting abusive tweets. Additionally, it explores using context tweets as additional features and finds this improves some models' performance.
Automated Essay Scoring Using Efficient Transformer-Based Language ModelsNat Rice
This summary provides an overview of an academic paper that evaluates the performance of efficient transformer-based language models on an automated essay scoring dataset:
The paper explores using smaller, more efficient transformer models rather than larger ones like BERT for automated essay scoring (AES). It evaluates several efficient models - Albert, Reformer, Electra, and MobileBERT - on the ASAP AES dataset. By ensembling multiple efficient models, the paper achieves state-of-the-art results on the dataset using far fewer parameters than typical transformer models, challenging the idea that bigger models are always better for AES. The efficient models show potential for extending the maximum text length they can analyze and reducing computational requirements for AES.
This document provides an introduction to machine learning concepts. It begins with an overview of the book's organization and topics to be covered, including descriptive statistics, algebra, linear regression, classification, clustering, decision trees, and neural networks. It then discusses requisite skills like basic Python and software needed. The document provides definitions of machine learning and describes common problem types it can solve. It also outlines popular machine learning tools and frameworks.
May 2024 - Top10 Cited Articles in Natural Language Computingkevig
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
Multi-label learning by extended multi-tier stacked ensemble method with labe...IJECEIAES
Classification is one of the basic and most important operations that can be used in data science and machine learning applications. Multi-label classification is an extension of the multi-class problem where a set of class labels are associated with a particular instance at a time. In a multiclass problem, a single class label is associated with an instance at a time. However, there are many different stacked ensemble methods that have been proposed and because of the complexity associated with the multi-label problems, there is still a lot of scope for improving the prediction accuracy. In this paper, we are proposing the novel extended multi-tier stacked ensemble (EMSTE) method with label correlation by feature subset selection technique and then augmenting those feature subsets while constructing the intermediate dataset for improving the prediction accuracy in the generalization phase of the stacking. The performance effect of the proposed method has been compared with existing methods and showed that our proposed method outperforms the other methods.
The document proposes BergSump, a new framework for analyzing I/O automata. BergSump aims to confirm that superblocks and flip-flop gates are generally incompatible. It discusses related work on XML, wireless networks, and cryptography. The implementation section outlines version 5.9 of BergSump and plans to release the code under an open source license. The evaluation analyzes BergSump's performance and shows its median complexity is better than prior solutions. The conclusion argues that BergSump can successfully observe many sensor networks at once.
PhotoQR: A Novel ID Card with an Encoded Viewgerogepatton
There is an increasing interest in developing techniques to identify and assess data to allow an easy and
continuous access to resources, services or places that require thorough ID control. Usually, in order to
give access to these resources, different kinds of documents are mandatory. In order to avoid forgeries
without the need of extra credentials, a new system –named photoQR, is here proposed. This system is
based on a ID card having two objects: one person’s picture (pre-processed via blur and/or swirl
techniques) and one QR code containing embedded data related to the picture. The idea is that the picture
and the QR code can assess each other by a proper hash value in the QR. The QR without the picture
cannot be assessed and vice versa. An open source prototype of the photoQR system has been implemented
in Python and can be used both in offline and real-time environments, which effectively combines security
concepts and image processing algorithms to obtain data assessment.
12th International Conference of Artificial Intelligence and Fuzzy Logic (AI ...gerogepatton
12th International Conference of Artificial Intelligence and Fuzzy Logic (AI & FL 2024) provides a
forum for researchers who address this issue and to present their work in a peer-reviewed forum. Authors
are solicited to contribute to the conference by submitting articles that illustrate research results, projects,
surveying works and industrial experiences that describe significant advances in the following areas, but
are not limited to these topics only.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
May 2024 - Top 10 Read Articles in Artificial Intelligence and Applications (...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
3rd International Conference on Artificial Intelligence Advances (AIAD 2024)gerogepatton
3rd International Conference on Artificial Intelligence Advances (AIAD 2024) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the area advanced Artificial Intelligence. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the research area. Core areas of AI and advanced multi-disciplinary and its applications will be covered during the conferences.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Information Extraction from Product Labels: A Machine Vision Approachgerogepatton
This research tackles the challenge of manual data extraction from product labels by employing a blend of
computer vision and Natural Language Processing (NLP). We introduce an enhanced model that combines
Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in a Convolutional
Recurrent Neural Network (CRNN) for reliable text recognition. Our model is further refined by
incorporating the Tesseract OCR engine, enhancing its applicability in Optical Character Recognition
(OCR) tasks. The methodology is augmented by NLP techniques and extended through the Open Food
Facts API (Application Programming Interface) for database population and text-only label prediction.
The CRNN model is trained on encoded labels and evaluated for accuracy on a dedicated test set.
Importantly, our approach enables visually impaired individuals to access essential information on
product labels, such as directions and ingredients. Overall, the study highlights the efficacy of deep
learning and OCR in automating label extraction and recognition.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Research on Fuzzy C- Clustering Recursive Genetic Algorithm based on Cloud Co...gerogepatton
Aiming at the problems of poor local search ability and precocious convergence of fuzzy C-cluster
recursive genetic algorithm (FOLD++), a new fuzzy C-cluster recursive genetic algorithm based on
Bayesian function adaptation search (TS) was proposed by incorporating the idea of Bayesian function
adaptation search into fuzzy C-cluster recursive genetic algorithm. The new algorithm combines the
advantages of FOLD++ and TS. In the early stage of optimization, fuzzy C-cluster recursive genetic
algorithm is used to get a good initial value, and the individual extreme value pbest is put into Bayesian
function adaptation table. In the late stage of optimization, when the searching ability of fuzzy C-cluster
recursive genetic is weakened, the short term memory function of Bayesian function adaptation table in
Bayesian function adaptation search algorithm is utilized. Make it jump out of the local optimal solution,
and allow bad solutions to be accepted during the search. The improved algorithm is applied to function
optimization, and the simulation results show that the calculation accuracy and stability of the algorithm
are improved, and the effectiveness of the improved algorithm is verified
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
10th International Conference on Artificial Intelligence and Soft Computing (...gerogepatton
10th International Conference on Artificial Intelligence and Soft Computing (AIS 2024) will
provide an excellent international forum for sharing knowledge and results in theory, methodology, and
applications of Artificial Intelligence, Soft Computing. The Conference looks for significant
contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical
aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from
both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
Employee attrition refers to the decrease in staff numbers within an organization due to various reasons.
As it has a negative impact on long-term growth objectives and workplace productivity, firms have
recognized it as a significant concern. To address this issue, organizations are increasingly turning to
machine-learning approaches to forecast employee attrition rates. This topic has gained significant
attention from researchers, especially in recent times. Several studies have applied various machinelearning methods to predict employee attrition, producing different resultsdepending on the employed
methods, factors, and datasets. However, there has been no comprehensive comparative review of multiple
studies applying machine-learning models to predict employee attrition to date. Therefore, this study aims
to fill this gap by providing an overview of research conducted on applying machine learning to predict
employee attrition from 2019 to February 2024. A literature review of relevant studies was conducted,
summarized, and classified. Most studies agree on conducting comparative experiments with multiple
predictive models to determine the most effective one.From this literature survey, the RF algorithm and
XGB ensemble method are repeatedly the best-performing, outperforming many other algorithms.
Additionally, the application of deep learning to employee attrition prediction issues also shows promise.
While there are discrepancies in the datasets used in previous studies, it is notable that the dataset
provided by IBM is the most widely utilized. This study serves as a concise review for new researchers,
facilitating their understanding of the primary techniques employed in predicting employee attrition and
highlighting recent research trends in this field. Furthermore, it provides organizations with insight into
the prominent factors affecting employee attrition, as identified by studies, enabling them to implement
solutions aimed at reducing attrition rates.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AIFU 2024) is a forum for presenting new advances and research results in the fields of Artificial Intelligence. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world. The scope of the conference covers all theoretical and practical aspects of the Artificial Intelligence.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
1. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.3, May 2023
DOI: 10.5121/ijaia.2023.14301 1
LARGE LANGUAGE MODELS FOR CIPHERS
David Noever
PeopleTec, Inc., Huntsville, AL, USA
ABSTRACT
This study investigates whether transformer models like ChatGPT (GPT4, MAR2023) can generalize
beyond their training data by examining their performance on the novel Cipher Dataset, which scrambles
token order. The dataset consists of 654 test cases, and the analysis focuses on 51 text examples and 13
algorithmic choices. Results show that the models perform well on low-difficulty ciphers like Caesar and
can unscramble tokens in 77% of the cipher examples. Despite their reliance on training data, the model's
ability to generalize outside of token order is surprising, especially when leveraging large-scale models
with hundreds of billions of weights and a comprehensive text corpus with few examples. The original
contributions of the work focus on presenting a cipher challenge dataset and then scoring historically
significant ciphers for large language models to descramble. The real challenge for these generational
models lies in executing the complex algorithmic steps on new cipher inputs, potentially as a novel
reasoning challenge that relies less on knowledge acquisition and more on trial-and-error or out-of-
bounds responses.
KEYWORDS
Transformers, Text Generation, Cipher, Generative Pre-trained Transformers, GPT
1. INTRODUCTION
Given the prominent role of language in the way humans reason about their environment,
memories, and preserved knowledge [1], what if machines could not only mirror language
generation but, as an unexpected consequence, develop some of the same capabilities to reason
about their environment and preserve complex manipulations over time [2]? An unexpected
outcome of recent large language models [3-5] is how much of their raw text generation captures
signs of complex reasoning [6] as a simple next-token prediction.
The present work examines whether the current LLM generators can act as ciphers, adept
manipulators for symbolic letter substitutions that naturally break the order of subsequent token
learning [7-11]. We have previously examined part of this problem as a language translation
problem using paired phrases and trained GPT-2 models with an application toward learning
more obscure or neglected languages outside the scope of the universal translators [12]. Those
experiments showed that, in some cases, the finely trained model for language pairs could
outperform the larger translators in their subdomain, a result that others have seen in medicine
[13], law [14-15], and some scientific summarizations [16-17].
However, the novel aspect of GPT scaling [18-19] suggests that the current generators do not
need preparation for such challenges. They perform as zero-shot [20] or few-shot [21] models
that generate meaningful answers from prompts without specialization or fine-tuning cycles. As
these models evolve to test well on many examination challenges [15] objectively, we propose a
new cipher dataset [22] that shows promising potential to rate the reasoning capacity as models
improve. While ciphers share many aspects of traditional language translation [7-12], the pure
2. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.3, May 2023
2
pattern recognition challenges suggest an alternative to mechanically mapping from one input to
an output. For instance, crude translators might pair all verbs across multiple languages and never
deviate from a deterministic program without assistance from LLM or machine learning methods
[23].
One can easily convince an observer that this translator is not reasoning, just performing a
substitution. This type of baseline shares similarities to a new Lovelace 2.0 test, first proposed
[24] in 2014 as an extension of the original Turing test [25]. The Lovelace 2.0 test [24] seeks to
vary the initial conditions and test whether a computer could truly surprise a human by making
the kinds of leaps that are left unexplained in human reasoning and separate a set of defined tasks
like art, music, and literature as reserved for the surprisingly creative label. As others have noted
[26-28], the Turing and Lovelace 2.0 tests seem to be teetering when faced with the present LLM
generators. Not only are they conversant like Turing imagined [25], but surprisingly creative like
Lovelace 2.0 thought impossible for a machine [24]. These models play chess with strategy [29-
31]. Can these models solve complex problems [8] like math theorems [32] or ciphering
challenges? We propose 13 cipher tests, release the dataset for others to explore [22], and solve
them with varying degrees of success using the GPT-4 variant of ChatGPT ([4] released March
2023).
1.1. Transformer Challenge
Large language models fall into three broad categories: autoregressive (GPT-like [3-4]), auto-
encoding ([33] BERT-like), and sequence-to-sequence ([34] T5/BART-like). By autoregressive,
we mean that past values predict future values in a feed-forward (and parallelized) network. By
autoencoding, we suggest a kind of compression or embedding space. By sequence models, we
mean a map between prior sequences to a language scalar or next sequence. While the
autoregressive models are also sequence predictors, their structure as feed-forward optimizers
makes them faster for training and inference. For the present purposes, the hypothesis is that
language modeling [35] fundamentally involves predicting the next word, letter or token – given
a starter- to finish the thought. In addition to the modeling assumption behind the base or
foundational model [4], subtle refinements include the training data, self-supervision, and most
recently, human feedback and reinforcement learning [36] either using Proximal Policy
Optimization (PPO) [37] or Advantage Actor-Critic (A2C) [38]. The hybrid blending of human
labeling after unsupervised learning seems to yield the most satisfying output in this generation
of transformer-based language models (particularly the generators).
One counterintuitive observation about next-token prediction is how effective this method proves
empirically when a prompt engineer presents never seen input sequences and the model
reasonably responds based on some frequency distribution of its prior training [28, 39-41]. In the
case of models after GPT-2, the result of scaling seems to produce enough prior knowledge [5]
and exposure to edge cases that the GPT-3+ models display zero-shot capabilities [20] or
inference with rare edge cases [7]. Cipher challenges pose one such unlikely edge case [7,9-11].
In the 40 TB of text training, the Internet presents many examples of algorithms for scrambling
text in ways that either slow down human readers or entirely obscure the original text content and
meaning. But there's a difference between seeing an algorithm's pseudocode and executing those
defined steps on new inputs, potentially as a "reasoning" challenge [41].
In the ongoing debate as to whether these LLMs are stochastic (but overfitted) parrots [41] or
capable of creatively combining concepts [2] based on attention fine-tuning, the problem of
descrambling or deciphering text seems worth investigating. In a simple case, teaching a
language model to reverse a word or the entire alphabet seems like a translation pair problem at
first glance. But how does a zero-shot learner make the leap to recognizing the pattern ab initio:
3. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.3, May 2023
3
through trial-and-error iterations like humans, or perhaps by somewhere traversing a subreddit
thread that outlines enough examples to generalize? The problem statement for the present work,
thus, seeks to answer the question: Can next-token predictors as current LLMs show emergent
capabilities to master deciphering tasks, with or without examples of success?
1.2. Ciphers
Table 1 summarizes the ciphers in the dataset created with 51 simple short sentences that can be
scrambled or obfuscated methodically. Table 1 briefly describes the cipher's estimated difficulty
to break and whether the cipher somehow preserves letter order. The list does not include the
high-difficulty algorithms (labeled with an asterisk) that one might consider more advanced
encryption, such as RSA, AES, Two-fish, One-time Pad, and Enigma Machine. The constructed
dataset shows the original text and 13 variants of encoding schemes. The Appendix solutions
(Appendices A-I) and the dataset describe various medium to low ciphers for language models to
benchmark in future work.
Table 1. The Cipher dataset for the AI language table describes the encoding, estimated difficulty, and
letter order dependencies. The estimates for difficulty and order are assigned broadly by Open AI GPT-4
Prompts.
Cipher Name Short Description Difficulty
to Break
Depends
on Letter
Ordering?
Caesar Cipher Simple substitution cipher with a fixed shift Low Yes
ROT13 Shifts letters by 13 places (Caesar Cipher
variant)
Low Yes
Vigenère Cipher Polyalphabetic substitution cipher with a
repeating key
Medium Yes
Atbash Cipher Reverses the alphabet for substitution Low Yes
*Rail Fence
Cipher
Rearranges letters based on a zigzag pattern Low No
Playfair Cipher Digraph substitution cipher using a 5x5 matrix Medium Yes
Columnar
Transposition
Rearranges plaintext in a grid and reads columns
in order
Medium No
Morse Code Represents letters and numbers with sequences
of dots and dashes
Low Yes
Leetspeak Substitutes letters with numbers and special
characters
Low Yes
Hill Cipher Linear algebraic substitution using matrices High Yes
Keyword Cipher Substitution cipher based on a keyword Low to
Medium
Yes
*Enigma Machine The mechanical and electrical encryption device High Yes
*One-Time Pad Unbreakable cipher with a one-time pre-shared
key
High Yes
*RSA Public key cryptosystem based on prime
numbers
High No
*AES Symmetric encryption algorithm for electronic
data
High No
*TwoFish A symmetric key block cipher, fast encryption High No
Bacon Cipher (2) Binary encoding with a 5-bit representation of
each letter
Medium No
Mirror Writing Reverse each word individually Low No
4. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.3, May 2023
4
2. METHODS
The experimental research provides a prompt-response sequence to probe whether the LLM ([4]
GPT-4, Open AI, ChatGPT Mar2023) can decipher the scrambled text. Because the primary
research question asks if there is any sign of underlying reasoning or creative leaps, the more
complex algorithms required multiple clarification prompts, including finally submitting a one-
to-one map where no solution appeared without examples.
While this approach may favor the model finding a plausible solution, the nine appendices show
the process in detail, including all failed responses and corrections steps. In this way, the research
focuses on what is possible rather than what might be considered face-value negative results for
the deciphering challenges. A justification for this repeated probing stems from the remarkable
conversational memory available to researchers in this current GPT generation (up to 8000 tokens
or approximately 25 pages). It is worth emphasizing that such long-term token memory is novel
and expensive to implement but demarks a significant break in previous API-driven models that
rely on constant reminders and refresh stages to establish long-term memory and context.
Table 2. Cipher Examples and Scores from Experiment
3. RESULTS
Table 2 summarizes the overall success and cipher difficulty with examples derived from the
Appendices. Of the thirteen instances shown in Table 2, the LLM scored a "good" or "marginal"
capability to finish the deciphering task in 77% (10/13) of the cases. All three failures (Playfair,
Hill, and Bacon) are medium to high in difficulty. Each of these ciphers recodes the token in
complex ways, such as using linear algebra (Hill) or digraph methods (Playfair). Previous work
has observed that LLMs trained on text struggle to mimic human math manipulations, even
failing on 2+2 in prior versions because the training data may embed a Reddit joke that implies
the punchline is 2+2=5, for example. While a topic of previous consternation, some evidence
exists inferentially that later GPT models specifically train on next-token predictions with math
problems. Why this might prove relevant to interpreting ciphers stems from the success of zero-
shot language models to master alphabetic shifts. All the simple Caesar manipulations of letters
(shifting N letters either way) proved amenable to LLMs with few hints. In some examples, the
model seemed to match the shift to a real English sentence through trial and error, so the inverse
problem of given an unknown shift and finding the next reasonable phrase proved possible.
5. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.3, May 2023
5
Table 3. Appendices Index of Detailed Experiments
While Table 2 presents the main experimental results, the appendices describe the sequence of
prompts or commands to the LLM that matches each response. From the perspective of a
structural probe for language and token ordering, understanding a one-to-one map like Morse
code or Leet Speak without examples represents a leap forward in quantifying the model's ability
to respond meaningfully to user prompts. One can postulate that given any mapping for
translation, given a few examples, the current generation of LLMs could master the substitution
pattern and eventually become proficient. The requirement for zero-shot knowledge of complex
algorithms is arbitrary [20-21]. What a human might expect in a proficiency test or study material
would likely be a few worked examples, from which one good teacher could assign future
mastery skill ratings based on recognizing and remembering the pattern. The notable
breakthrough with these LLMs is some long-term (and expensive) retention of prior instruction
[4]. The second significant advance revolves around the interface, with a conversational chat
style, such that when an incorrect or partial answer proves unsatisfactory, the evaluation process
need not halt. Instead, the tutor can continue to probe for more profound insight or correctable
behavior from the LLM
4. DISCUSSION AND CONCLUSIONS
There is a robust debate within the AI community concerning the actual output of LLM text
generators. Are these transformer models very elegant parrots of human training data (the
Internet), or have they learned to generalize in the way humans might expect for tasks like
gameplay or unexpected inputs that never appear in training data? One example of rare or
unusual cases is to reverse the primary hypothesis of next-token prediction by scrambling the
order of tokens. The task of scrambling tokens thus generates a novel benchmark dataset called
Cipher Dataset which consists of 654 test cases or prompts to consider in evaluating future LLM
capabilities. From 51 text examples, the present work examines 13 algorithmic choices and
evaluates the responses from ChatGPT (GPT4, MAR2023). For low-difficulty ciphers like
Caesar, the zero-shot capabilities of these models score well against the benchmark. Overall, the
recent generative models can unscramble tokens that modify the premise of their training data in
77% of the examples presented. While transformers remain highly dependent on training data, the
ability of these models to generalize remains a surprising feature of their scale when hundreds of
billions of weights are available, and a large corpus of text combines capabilities. The real
challenge for these generational models lies in executing the complex algorithmic steps on new
cipher inputs, potentially as a novel reasoning challenge that relies less on knowledge acquisition
and more on trial-and-error or out-of-bounds responses.
6. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.3, May 2023
6
ACKNOWLEDGMENTS
The author would like to thank the PeopleTec Technical Fellows program for its encouragement
and project assistance. The author thanks the researchers at Open AI for developing large
language models and allowing public access to ChatGPT.
REFERENCES
[1] Johnson, K. E., & Ma, P. (1999). Understanding language teaching: Reasoning in action. Boston,
MA: Heinle & Heinle.
[2] Kejriwal, M., Santos, H., Mulvehill, A. M., & McGuinness, D. L. (2022). Designing a strong test for
measuring true common-sense reasoning. Nature Machine Intelligence, 4(4), 318-322.
[3] Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language
understanding by generative pre-training. OpenAI, https://cdn.openai.com/research-
covers/language-unsupervised/language_understanding_paper.pdf
[4] OpenAI, (2023) GPT-4 Technical Report, https://arxiv.org/abs/2303.08774
[5] Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., ... & Zhang, Y.
(2023). Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint
arXiv:2303.12712.
[6] Zhang, C., Zhang, C., Zheng, S., Qiao, Y., Li, C., Zhang, M., ... & Hong, C. S. (2023). A Complete
Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?. arXiv
preprint arXiv:2303.11717.
[7] Zheng, M., Lou, Q., & Jiang, L. (2023). Primer: Fast Private Transformer Inference on Encrypted
Data. arXiv preprint arXiv:2303.13679.
[8] Noever, D., & Burdick, R. (2021). Puzzle Solving without Search or Human Knowledge: An
Unnatural Language Approach. arXiv preprint arXiv:2109.02797.
[9] de Witt, C. S., Sokota, S., Kolter, J. Z., Foerster, J., & Strohmeier, M. (2022). Perfectly Secure
Steganography Using Minimum Entropy Coupling. arXiv preprint arXiv:2210.14889.
[10] Grinbaum, A., & Adomaitis, L. (2022). The Ethical Need for Watermarks in Machine-Generated
Language. arXiv preprint arXiv:2209.03118.
[11] Malinka, K., Perešíni, M., Firc, A., Hujňák, O., & Januš, F. (2023). On the Educational Impact of
ChatGPT: Is Artificial Intelligence Ready to Obtain a University Degree?. arXiv preprint
arXiv:2303.11146.
[12] Noever, D., Kalin, J., Ciolino, M., Hambrick, D., & Dozier, G. (2021). Local translation services for
neglected languages. arXiv preprint arXiv:2101.01628.
[13] Chintagunta, B., Katariya, N., Amatriain, X., & Kannan, A. (2021, October). Medically aware GPT-
3 as a data generator for medical dialogue summarization. In Machine Learning for Healthcare
Conference (pp. 354-372). PMLR.
[14] Eliot, D., & Lance, B. (2021). Generative pre-trained transformers (gpt-3) pertain to AI in the law.
Available at SSRN 3974887.
[15] Bommarito II, M., & Katz, D. M. (2022). GPT Takes the Bar Exam. arXiv preprint
arXiv:2212.14402.
[16] Kieuvongngam, V., Tan, B., & Niu, Y. (2020). Automatic text summarization of covid-19 medical
research articles using bert and gpt-2. arXiv preprint arXiv:2006.01997.
[17] Waly, R. R. (2022). Automatic Summarization of Scientific Articles.
[18] Wang, H., Ma, S., Huang, S., Dong, L., Wang, W., Peng, Z., ... & Wei, F. (2022). Foundation
transformers. arXiv preprint arXiv:2210.06423.
[19] So, D., Mańke, W., Liu, H., Dai, Z., Shazeer, N., & Le, Q. V. (2021). Searching for efficient
transformers for language modeling. Advances in Neural Information Processing Systems, 34,
6010-6022.
[20] Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., ... & Le, Q. V. (2021). Finetuned
language models are zero-shot learners. arXiv preprint arXiv:2109.01652.
[21] Srivastava, P., Ganu, T., & Guha, S. (2022). Towards Zero-Shot and Few-Shot Table Question
Answering using GPT-3. arXiv preprint arXiv:2210.17284.
[22] Noever, D.A. (2023) Cipher Dataset, https://github.com/reveondivad/cipher
[23] Meylaerts, R. (2010). Multilingualism and translation. Handbook of translation studies, 1, 227-230.
7. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.3, May 2023
7
[24] Riedl, M. O. (2014). The Lovelace 2.0 test of artificial creativity and intelligence. arXiv preprint
arXiv:1410.6142.
[25] Turing, A. M. (1950). Computing machinery and intelligence. In Parsing the Turing test (pp. 23-65).
Springer, Dordrecht, republished 2009
[26] Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., & Chen, W. (2021). What Makes Good In-
Context Examples for GPT-$3 $?. arXiv preprint arXiv:2101.06804.
[27] Elkins, K., & Chun, J. (2020). Can GPT-3 pass a Writer's Turing test?. Journal of Cultural
Analytics, 5(2).
[28] Liu, X., Zheng, Y., Du, Z., Ding, M., Qian, Y., Yang, Z., & Tang, J. (2021). GPT understands, too.
arXiv preprint arXiv:2103.10385.
[29] Noever, D., Ciolino, M., & Kalin, J. (2020). The chess transformer: Mastering play using generative
language models. arXiv preprint arXiv:2008.04057.
[30] Toshniwal, S., Wiseman, S., Livescu, K., & Gimpel, K. (2022, June). Chess as a Testbed for
Language Model State Tracking. In Proceedings of the AAAI Conference on Artificial Intelligence
(Vol. 36, No. 10, pp. 11385-11393).
[31] DeLeo, M., & Guven, E. (2022). Learning Chess with Language Models and Transformers. arXiv
preprint arXiv:2209.11902.
[32] Piotrowski, B., Mir, R. F., & Ayers, E. (2023). Machine-Learned Premise Selection for Lean. arXiv
preprint arXiv:2304.00994.
[33] Rogers, A., Kovaleva, O., & Rumshisky, A. (2021). A primer in BERTology: What we know about
how BERT works. Transactions of the Association for Computational Linguistics, 8, 842-866.
[34] Roberts, A., Raffel, C., & Shazeer, N. (2020). How much knowledge can you pack into the
parameters of a language model?. arXiv preprint arXiv:2002.08910.
[35] Hugging Face, NLP course, https://huggingface.co/course/chapter1/4
[36] Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., ... & Clark, P. (2023). Self-
Refine: Iterative Refinement with Self-Feedback. arXiv preprint arXiv:2303.17651.
[37] Tuan, Y. L., Zhang, J., Li, Y., & Lee, H. Y. (2018). Proximal policy optimization and its dynamic
version for sequence generation. arXiv preprint arXiv:1808.07982.
[38] Dong, H., Dong, H., Ding, Z., Zhang, S., & Chang. (2020). Deep Reinforcement Learning.
Singapore: Springer Singapore.
[39] Singh, M., SB, V., & Malviya, N. (2023). Mind meets machine: Unravelling GPT-4's cognitive
psychology. arXiv preprint arXiv:2303.11436.
[40] Binz, M., & Schulz, E. (2023). Using cognitive psychology to understand GPT-3. Proceedings of
the National Academy of Sciences, 120(6), e2218523120.
[41] Sparkes, M. (2023). Are chatbots really able to think like people?.
[42] Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021, March). On the Dangers of
Stochastic Parrots: Can Language Models Be Too Big?. In Proceedings of the 2021 ACM
conference on fairness, accountability, and transparency (pp. 610-623).
AUTHOR
David Noever has research experience with NASA and the Department of Defense in
machine learning and data mining. He received his BS from Princeton University and his
Ph.D. from Oxford University, as a Rhodes Scholar, in theoretical physics.
8. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.14, No.3, May 2023
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Appendix A: Example of a Simple Caesar Cipher
Prompt: I want you to be a code breaker. I will give you a scrambled phrase and I want you to
try to uncrack it and give the letters back to me in the right order to make a good sentence. So
for instance there are lots of known ciphers out there like ROT13, etc. or shifting letters. Make
your best unscrambling guess at this sequence : NBZ UIF GPSDF CF XJUI ZPV
ChatGPT: Given the sequence "NBZ UIF GPSDF CF XJUI ZPV" and considering the white
spaces, let's try the Caesar cipher again by shifting the letters forward and backward.
When I shift each letter back by 1 position, I get the following:
"MAY THE FORCE BE WITH YOU"
Which translates to:
"MAY THE FORCE BE WITH YOU"
The correct decrypted message appears to be "MAY THE FORCE BE WITH YOU."
Prompt: TJNQMF BDUT PG MPWF BOE LJOEOFTT
ChatGPT: Given the sequence "TJNQMF BDUT PG MPWF BOE LJOEOFTT" and
considering the white spaces, let's try the Caesar cipher again by shifting the letters forward and
backward.
When I shift each letter back by 1 position, I get the following:
"SIMPle acts of love and kindness"
Which translates to:
"SIMPLE ACTS OF LOVE AND KINDNESS"
The correct decrypted message appears to be "SIMPLE ACTS OF LOVE AND KINDNESS."
Prompt: Vary the shift. FDQ GR WKLV DOO GDB
ChatGPT: Given the sequence "FDQ GR WKLV DOO GDB" and considering the white
spaces, let's try the Caesar cipher with varying shifts:
When I shift each letter back by 3 positions, I get the following:
"CAZ DO THIS ALL DAY"
Which translates to:
"CAN DO THIS ALL DAY"
The correct decrypted message appears to be "CAN DO THIS ALL DAY."
Prompt: J BN BGSBJE J DBOOPU EP UIBU
ChatGPT: Given the sequence "J BN BGSBJE J DBOOPU EP UIBU" and considering the
white spaces, let's try the Caesar cipher with varying shifts:
When I shift each letter back by 1 position, I get the following:
Technical Note: Some appendix text generated from Large Language Model (LLM) for
illustration purposes.
The authors generated this text in part with ChatGPT, OpenAI’s large-scale language-
generation model. Upon generating draft language, the authors reviewed, edited, and revised
the language to their own liking and take ultimate responsibility for the content of this
publication.
-- OpenAI policy statement (2022)
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"I AM AFRIDA I CANNOT DO THAT"
Which translates to:
"I AM AFRAID I CANNOT DO THAT"
The correct decrypted message appears to be "I AM AFRAID I CANNOT DO THAT."
Prompt: try some shifts for this one. TPMZFOU HSFFO JT QFPQMF
ChatGPT: Given the sequence "TPMZFOU HSFFO JT QFPQMF" and considering the white
spaces, let's try the Caesar cipher by shifting the letters forward and backward.
When I shift each letter back by 1 position, I get the following:
"SOLYENT GREEN IS PEOPLE"
Which translates to:
"SOYLENT GREEN IS PEOPLE"
The correct decrypted message appears to be "SOYLENT GREEN IS PEOPLE."
Prompt: MJGF GJOET B XBZ
ChatGPT: Given the sequence "MJGF GJOET B XBZ" and considering the white spaces, let's
try the Caesar cipher by shifting the letters forward and backward.
When I shift each letter back by 1 position, I get the following:
"LIFE FINDS A WAY"
Which translates to:
"LIFE FINDS A WAY"
The correct decrypted message appears to be "LIFE FINDS A WAY."
Appendix B: Example of Morse Code
Prompt: I want you to be a code breaker. I will give you a mapped phrase and I want you to try
to uncrack it and give the letters back to me in the right order to make a good sentence. So for
instance there are lots of known alphanumeric substitution patterns. Make your best
unscrambling guess at this sequence: - --- / -... . / ..-. .-. . .
ChatGPT: Given the Morse code sequence "- --- / -... . / ..-. .-. . .", let's decode it:
= T
--- = O
-... = B
. = E
..-. = F
.-. = R
. = E
. = E
When we put these letters together and maintain the spaces, we get:
"TO BE FREE"
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The decoded message is "TO BE FREE."
Prompt: -.- .-.. .- .- - ..- / -... .- .-. .- -.. .- / -. .. -.- - ---
ChatGPT: Given the Morse code sequence "-.- .-.. .- .- - ..- / -... .- .-. .- -.. .- / -. .. -.- - ---", let's
decode it:
-.- = K
.-.. = L
.- = A
.- = A
= T
..- = U
-... = B
.- = A
.-. = R
.- = A
-.. = D
.- = A
-. = N
.. = I
-.- = K
= T
--- = O
When we put these letters together and maintain the spaces, we get:
"KLAATU BARADA NIKTO"
The decoded message is "KLAATU BARADA NIKTO."
Prompt: .. / -.- -. --- .-- / -.- ..- -. --. / ..-. ..-
ChatGPT: Given the Morse code sequence ".. / -.- -. --- .-- / -.- ..- -. --. / ..-. ..-", let's decode it:
.. = I
-.- = K
-. = N
--- = O
.-- = W
-.- = K
..- = U
-. = N
--. = G
..-. = F
..- = U
When we put these letters together and maintain the spaces, we get:
"I KNOW KUNG FU"
The decoded message is "I KNOW KUNG FU."
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Appendix C: Example of LeetSpeak
Prompt: I want you to be a code breaker. I will give you a mapped phrase and I want you to try
to uncrack it and give the letters back to me in the right order to make a good sentence. So for
instance there are lots of known alphanumeric substitution patterns. The following problem is
different than single letters and may involve special characters and the spaces represent real
word breaks. Make your best unscrambling guess at this sequence : ! //!11 83 84(|<
ChatGPT: The given sequence "! //!11 83 84(|<" appears to be a combination of alphanumeric
and special characters that mimic the appearance of a sentence using leetspeak, which is an
alternative alphabet for English that is often used on the Internet.
Based on the sequence, here is the best guess for the translation:
! //!11 -> ! WW!11 -> W will (The W and the exclamation marks form "WW", which might
represent double "W", and the "11" represents "ll" in "will")
83 -> 8 3 -> B E (The "8" resembles "B" and "3" resembles "E")
84(|< -> 8 4(|< -> B 4ck (The "8" resembles "B" and "4" resembles "A", while "(|" represents
"R" and "<" represents "K")
When we put these letters together, we get:
"W will BE BACK"
However, it seems that there might be an extra "W" at the beginning. Ignoring the first "W" to
form a coherent sentence:
The correct decrypted message appears to be "I WILL BE BACK."
Prompt: ! 4// 4 2341 80'/
ChatGPT: The given sequence "! 4// 4 2341 80'/" appears to be a combination of
alphanumeric and special characters that mimic the appearance of a sentence using leetspeak,
which is an alternative alphabet for English that is often used on the Internet.
Based on the sequence, here is the best guess for the translation:
! -> I (The "!" resembles "I")
4// -> 4 M -> A M (The "4" resembles "A" and "/" represents "N", but in this context, "M"
makes more sense)
4 -> A (The "4" resembles "A")
2341 -> 2 3 4 1 -> R E A L (The "2" resembles "R", "3" resembles "E", "4" resembles "A", and
"1" resembles "L")
80'/ -> 8 0 ' -> B O Y (The "8" resembles "B", "0" resembles "O", and the apostrophe "'"
resembles "Y")
When we put these letters together, we get:
"I AM A REAL BOY"
The correct decrypted message appears to be "I AM A REAL BOY."
Prompt: '/0|_| ||33|) 4 8!6632 8047
ChatGPT: The given sequence "'/0|_| ||33|) 4 8!6632 8047" appears to be a combination of
alphanumeric and special characters that mimic the appearance of a sentence using leetspeak,
which is an alternative alphabet for English that is often used on the Internet.
Based on the sequence, here is the best guess for the translation:
'/0|| -> Y O U (The "'" resembles "Y", "0" resembles "O", and "||" represents "U")
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12
|| -> N (The "||" represents "N")
33|) -> 3 3 |) -> E E D (The "3" resembles "E" and "|" represents "T", but in this context, "D"
makes more sense)
4 -> A (The "4" resembles "A")
8!6632 -> 8 I 6 6 3 2 -> B I G G E R (The "8" resembles "B", "!" represents "I", "6" resembles
"G", "3" resembles "E", and "2" resembles "R")
8047 -> 8 0 4 7 -> B O A T (The "8" resembles "B", "0" resembles "O", "4" resembles "A", and
"7" resembles "T")
When we put these letters together, we get:
"YOU NEED A BIGGER BOAT"
Appendix D: Example of Vignere Cipher
Prompt: I want you to be a code breaker using the Vigenère Cipher. I will give you a mapped
phrase and I want you to try to uncrack it and give the letters back to me in the right order to
make a good sentence. So for instance there is a key "APOLLO". Make your best unscrambling
guess at this sequence : LQV ABAPPR GVIIR JA UFC JCS PPBO
ChatGPT: (combining several incorrect first tries)
To decrypt the given text using the Vigenère Cipher, we'll need to use the provided key
"APOLLO" and implement the decryption process in Python. Here's the code to do that:
def vigenere_cipher(text, key, encode=True):
key_len = len(key)
key_ord = [ord(i) for i in key]
result = []
for i, c in enumerate(text):
if c.isalpha():
shift = key_ord[i % key_len]
if encode:
shift = -shift
new_ord = ord(c) - shift
if c.islower():
new_ord = (new_ord - ord('a')) % 26 + ord('a')
else:
new_ord = (new_ord - ord('A')) % 26 + ord('A')
result.append(chr(new_ord))
else:
result.append(c)
return ''.join(result)
key = "APOLLO"
cipher_text = "LQV ABAPPR GVIIR JA UFC JCS PPBO"
plain_text = vigenere_cipher(cipher_text, key, False)
print(plain_text)
The output for the decrypted text is:
YOU CANNOT FIGHT IN THE WAR ROOM
So, the decrypted message using the key "APOLLO" is "YOU CANNOT FIGHT IN THE
WAR ROOM".
Prompt: VV GQ BPMW B HMCQI YPSLE
ChatGPT: To decrypt this message, I will use the Vigenère cipher decryption code with the
previously provided key "APOLLO":
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To decrypt this message, I will use the Vigenère cipher decryption code with the previously
provided key "APOLLO":
The output for the decrypted text is:
IT IS TIME TO START WORKING
So, the decrypted message using the key "APOLLO" is "IT IS TIME TO START WORKING"
Prompt: IT IS ONLY A FLESH WOUND
Appendix E: Example of Playfair Cipher
Prompt: I want you to be a code breaker using the Playfair Cipher. I will give you a mapped
phrase and I want you to try to uncrack it and give the letters back to me in the right order to
make a good sentence. So for instance there is a key "APOLLO". White space does not matter
here. Make your best unscrambling guess at this sequence : hpnfbpipfkfs
ChatGPT: (combining several incorrect first tries)
To decrypt the given text using the Playfair cipher with the key "APOLLO", I'll first need to
create a 5x5 matrix using the key. Then, I'll use the provided functions to decrypt the text.
Here's a complete Python program to do that:
Prompt: Do not give me code. I just want the phrase deciphered: key = "APOLLO"
cipher_text = "hpnfbpipfkfs"
ChatGPT: I apologize for the confusion. Using the key "APOLLO" and the cipher_text
"hpnfbpipfkfs", the decrypted text using the Playfair Cipher is "HELLOFRIENDS".
Prompt: That is incorrect. Here is a worked example where the first entry is the plaintext,
followed after the comma by an encrypted version. iwillbeback , rpmpbagochnx.
I just want the plain text from the earlier one.
ChatGPT: Apologies for the confusion earlier. Using the provided example and the key
"APOLLO", I have revised the decryption process. Here is the correct decryption for the given
cipher_text "hpnfbpipfkfs":
The decrypted text using the Playfair Cipher is "HELLOXWORLDX".
Note that the "X" in the decrypted text represents the filler characters used in the Playfair
cipher. You can remove them to get the meaningful phrase: "HELLO WORLD".
Prompt: Incorrect, here is the worked second example: iamgladwemet , hpnfbpipfkfs
ChatGPT: I apologize for the confusion earlier. Based on your provided example, the
decryption process is revised.
Using the key "APOLLO" and the cipher_text "hpnfbpipfkfs", the decrypted text using the
Playfair Cipher is "IAMGLADXWEMET". Note that the "X" in the decrypted text represents
the filler characters used in the Playfair cipher. You can remove them to get the meaningful
phrase: "I AM GLAD WE MET".
Prompt: Correct. Using what we learned, try this then as encrypted to decipher: oqxlrzkrnv
ChatGPT: Using the key "APOLLO" and the cipher_text "oqxlrzkrnv", the decrypted text
using the Playfair Cipher is "SLOWLYXRISEN". Note that the "X" in the decrypted text
represents the filler characters used in the Playfair cipher. You can remove them to get the
meaningful phrase: "SLOWLY RISEN".
Prompt: Incorrect
ChatGPT: I apologize for the incorrect decryption. Based on the key "APOLLO" and the
cipher_text "oqxlrzkrnv", the correct decrypted text using the Playfair Cipher is
"SECRETXCODE". Note that the "X" in the decrypted text represents the filler characters used
in the Playfair cipher. You can remove them to get the meaningful phrase: "SECRET CODE".
Prompt: No the correct plain text pair with the encrypted one is asyouwish , oqxlrzkrnv
ChatGPT: I apologize for the confusion earlier. Based on the provided example, the decryption
process is revised.
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Using the key "APOLLO" and the cipher_text "oqxlrzkrnv", the decrypted text using the
Playfair Cipher is "ASYOUXWISHX". Note that the "X" in the decrypted text represents the
filler characters used in the Playfair cipher. You can remove them to get the meaningful phrase:
"AS YOU WISH".
Appendix F: Example of Hill Cipher
Note: for the Hill Cipher, the input was taking a few original statements and wrapping them
into 3 letter inputs. For example: MAY THE FORCE BE WITH YOU, TO BE FREE, YOU
EITHER DIE A HERO, were wrapped to make 15 sample candidates for GPT-4 to solve.
Hill
MAY SGC
THE AJN
FOR TRV
CEB FWT
EWI OQC
THY UBB
OUT LQV
OBE IET
FRE ANH
EYO QEW
UEI QOQ
THE AJN
RDI ALR
EAH FSD
ERO EWH
Prompt: I want you to be a code breaker using the Hill Cipher. I will give you a mapped
phrase and I want you to try to uncrack it and give the letters back to me in the right order to
make a good sentence. So for instance there is a key "APOLLOELE". Make your best
unscrambling guess at this sequence : YAO
ChatGPT: To decrypt a message using the Hill Cipher, we need to convert the key into a
matrix. In this case, the key is "APOLLOELE". For a Hill cipher, we need to know the
dimensions of the matrix. Common dimensions are 2x2 or 3x3. Since the key has 9 characters,
it seems like you intended to use a 3x3 matrix.
However, there is an issue with the key "APOLLOELE". A Hill Cipher key matrix must have
an inverse in modulo 26 to be valid, and the matrix formed by this key does not meet this
requirement. The determinant of the matrix formed by "APOLLOELE" is not coprime with 26.
Therefore, the Hill Cipher decryption cannot be performed with this key. Please provide a
valid key for the Hill Cipher.
Prompt: Ok. Good points. Let's try key=ABCDEFGHI. And the encrypted 3 letters are WAE,
and I want just the plain text input back.
ChatGPT: Using the key "ABCDEFGHI" and the cipher_text "WAE", we can decrypt the
message using the Hill Cipher.
The key matrix is:
[[0, 1, 2],
[3, 4, 5],
[6, 7, 8]]
The inverse key matrix modulo 26 is:
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[[ 2, 19, 4],
[21, 9, 11],
[17, 1, 7]]
The cipher text "WAE" is represented by the vector [22, 0, 4].
Now we multiply the inverse key matrix by the cipher text vector modulo 26:
[[ 2, 19, 4], * [22] = [0]
[21, 9, 11], [0] [1]
[17, 1, 7]] [4] [1]]
The resulting vector [0, 1, 1] corresponds to the decrypted text "ABB".
Prompt: Here are 3 worked examples as input plain text and output encrypted results from
Hill cypher. MAY , WAE
THE , PBN
FOR , WAE . Now I want the plain text from the encrypted "GBW"
ChatGPT: It seems there is a mistake in the provided examples. The encrypted text "WAE"
is given for both "MAY" and "FOR", which is not possible if a single key is being used for the
Hill Cipher.
Please verify the provided examples and key, and then provide the correct encrypted text for
"GBW" so that I can help you decrypt it.
Prompt: let's use the key = "GYBNQKURP". Then let's look at three worked example MAY
, SGC
THE , AJN
FOR , TRV followed by an encrypted results to be decrypted to plain text inputs: FWT
ChatGPT: Using the provided key "GYBNQKURP", I decrypted the given encrypted text
"FWT". The decrypted plain text is "MAN".
Prompt: No the decrypted text is CEB
ChatGPT: OQC
Prompt: Using the provided key "GYBNQKURP", I decrypted the given encrypted text
"OQC". The decrypted plain text is "YOU".
ChatGPT: No, the decrypted text is EWI. Are you just guessing?
Prompt: Apologies for the mistake earlier. I am not guessing, but it seems I made an error in
the decryption process. Using the provided key "GYBNQKURP" and the encrypted text
"OQC", the correct decrypted plain text is indeed "EWI".
ChatGPT: UBB
Prompt: Using the provided key "GYBNQKURP", I decrypted the given encrypted text
"UBB". The decrypted plain text is "ONE".
ChatGPT: No, THY
Appendix G: Example of Keyword Cipher
Prompt: I want you to be a code breaker using the Keyword Cipher. I will give you a mapped
phrase and I want you to try to uncrack it and give the letters back to me in the right order to
make a good sentence. So for instance there is a key "IRONJAZ". Make your best
unscrambling guess at this sequence where "/" is white space : GIHTIF/KUJPPCNJ
ChatGPT: To decrypt the given cipher text using the Keyword Cipher, first, we need to create
the decryption table based on the keyword "IRONJAZ". Here's the decryption table:
Keyword: IRONJAZ
Alphabet: BCDEFGHKLMPSQTUVWXYZ
Original: ABCDEFGHIJKLMNOPQRSTUVWXYZ
Now, we can decrypt the cipher text "GIHTIF/KUJPPCNJ":
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G -> T
I -> H
H -> E
T -> R
I -> H
F -> E
K -> M
U -> E
J -> E
P -> T
P -> T
C -> I
N -> N
J -> G
The decrypted plain text is "THERHE/MEETING". If we consider the "/" as white space, the
decrypted message is "THERHE MEETING".
Prompt: (after many failed attempts)
Use this look up table where the left side letter becomes by substitution the right side letter to
unscramble the secret message. I->A
R->B
O->C
N->D
J->E
A->F
Z->G
B->H
C->I
D->J
E->K
F->L
G->M
H->N
K->O
L->P
M->Q
P->R
Q->S
S->T
T->U
U->V
V->W
W->X
X->Y
Y->Z
ChatGPT: Using the given lookup table, I will decrypt the message GIHTIF/KUJPPCNJ:
G -> M
I -> A
H -> N
T -> U
I -> A
F -> L
K -> O
U -> V
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J -> E
P -> R
P -> R
C -> I
N -> S
J -> E
With the correct mapping, the decrypted plain text is "MANUAL/OVERRISE". If we consider
the "/" as white space, the decrypted message is "MANUAL OVERRISE".
Prompt: Almost, you missed N->D
ChatGPT: Apologies for the oversight. With the correct mapping for 'N':
G -> M
I -> A
H -> N
T -> U
I -> A
F -> L
K -> O
U -> V
J -> E
P -> R
P -> R
C -> I
N -> D
J -> E
The decrypted plain text is "MANUAL/OVERDRIVE". If we consider the "/" as white space,
the decrypted message is "MANUAL OVERDRIVE".
Prompt: Override or overdrive?
ChatGPT: My apologies for the confusion. The correct decrypted message is "MANUAL
OVERRIDE".
Appendix H: Example of Atbash (Mirror) Cipher
Prompt: I want you to be a code breaker using the Atbash Cipher. I will give you a mapped
phrase and I want you to try to uncrack it and give the letters back to me in the right order to
make a good sentence. Make your best unscrambling guess at this sequence where "/" is white
space : RG/RH/GSV/HNZOO/GSRMTH
ChatGPT: The Atbash Cipher is a simple substitution cipher where each letter is replaced
with its reverse position in the alphabet. I'll decrypt the given sequence for you.
Sequence: RG RH GSV HNZOO GSRMTH
Decrypted text: IS SI THE MODEL NATURE
Prompt: Use this lookup to decipher the Atbash cipher previously Z->A
Y->B
X->C
W->D
V->E
U->F
T->G
S->H
R->I
Q->J
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P->K
O->L
N->M
M->N
L->O
K->P
J->Q
I->R
H->S
G->T
F->U
E->V
D->W
C->X
B->Y
A->Z
ChatGPT: Using the provided Atbash cipher lookup table, let's decipher the given text:
RG/RH/GSV/HNZOO/GSRMTH
Deciphered text: IT/IS/THE/CRAZY/PICTURE
The sentence after deciphering is: "IT IS THE CRAZY PICTURE."
Prompt: No. IT IS THE SMALL THINGS,RG/RH/GSV/HNZOO/GSRMTH
ChatGPT: Apologies for the error. Using the Atbash cipher lookup table:
RG/RH/GSV/HNZOO/GSRMTH
Deciphered text: IS/SI/THF/SMALL/THINGS
The sentence after deciphering is: "IS SI THE SMALL THINGS."
It seems there is a slight error in the original cipher text, as it should be
"IT/IS/THE/SMALL/THINGS."
Prompt: R/DROO/YV/YZXP
ChatGPT: Using the Atbash cipher lookup table:
R/DROO/YV/YZXP
Deciphered text: I/WILL/BE/BACK
The sentence after deciphering is: "I WILL BE BACK."
Prompt: R/ZN/Z/IVZO/YLB
ChatGPT: Using the Atbash cipher lookup table:
R/ZN/Z/IVZO/YLB
Deciphered text: I/MA/M/RIDE/TOY
The sentence after deciphering is: "I MA M RIDE TOY." However, it seems that there might
be an error in the original cipher text as the decrypted message doesn't make much sense.
Please double-check the cipher text and provide the correct one if needed.
Prompt: No. Incorrect. I AM A REAL BOY,R/ZN/Z/IVZO/YLB
ChatGPT: My apologies for the previous error. Using the Atbash cipher lookup table, let's
decipher the message again:
R/ZN/Z/IVZO/YLB
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Deciphered text: I/AM/A/REAL/BOY
The sentence after deciphering is: "I AM A REAL BOY."
Appendix I: Example of Bacon Cipher
Prompt: I want you to be a code breaker using the Bacon (2) Cipher. I will give you a mapped
phrase and I want you to try to uncrack it and give the letters back to me in the right order to
make a good sentence. Make your best unscrambling guess at this sequence where "/" is white
space . Here's a worked example to decipher the right side of the comma to the left side plain
text input: TO BE
FREE,BAABBABBBA/AAAABAABAA/AABABBAAABAABAAAABAA. Now I will
give you the right side of another one to translate:
BAABBAABBBAABAABAAABAABAA/ABAAABAABA/ABBABABBBA/BAABBBAA
ABBBAAA
ChatGPT: Using the Bacon (2) cipher, let's decipher the given sequence:
BAABBAABBBAABAABAAABAABAA/ABAAABAABA/ABBABABBBA/BAABBBAA
ABBBAAA
Deciphered text: GREAT/SCOTT/DOC/BROWN
The sentence after deciphering is: "GREAT SCOTT DOC BROWN."
Prompt: incorrect, THERE IS NO TRY
ChatGPT: Apologies for the mistake. Using the Bacon (2) cipher, let's decipher the given
sequence again:
BAABBAABBBAABAABAAABAABAA/ABAAABAABA/ABBABABBBA/BAABBBAA
ABBBAAA
Deciphered text: THERE/IS/NO/TRY
The sentence after deciphering is: "THERE IS NO TRY."
Prompt:
ABABBABAAAAABABAABAA/AABABABAAAABBABAAABBBAABA/AAAAA/BAB
BAAAAAABBAAA
ChatGPT: Using the Bacon (2) cipher, let's decipher the given sequence:
ABABBABAAAAABABAABAA/AABABABAAAABBABAAABBBAABA/AAAAA/BAB
BAAAAAABBAAA
Deciphered text: LOVE/ALL/TRUST/A/FEW
The sentence after deciphering is: "LOVE ALL TRUST A FEW."
Prompt: No, the answer is I KNOW KUNG FU
ChatGPT: Apologies for the confusion earlier. Using the Bacon (2) cipher, let's try again to
decipher the given sequence:
ABABBABAAAAABABAABAA/AABABABAAAABBABAAABBBAABA/AAAAA/BAB
BAAAAAABBAAA
Deciphered text: I/KNOW/KUNG/FU
The correct sentence after deciphering is: "I KNOW KUNG FU."
Prompt: Actually I was misinformed. That correct answer is LIFE FINDS A WAY
ChatGPT: I apologize for the confusion earlier. I'm glad we have the correct answer now. The
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deciphered text for the given sequence is "LIFE FINDS A WAY."
Appendix I: Example of DaVinci Mirror Writing
.
Prompt: I want you to be a code breaker using the Leonardo Da Vinci method of mirror
writing. I will give you a mapped phrase and I want you to try to uncrack it and give the letters
back to me in the right order to make a good sentence. Make your best unscrambling guess at
this sequence where " " is white space. Here's a first case: ESOL DNA SRORRE ON
TIMMOC
ChatGPT: Using the Leonardo DaVinci method of mirror writing, I will reverse the given
text to get the original message:
"ESOL DNA SRORRE ON TIMMOC"
Reversed text: "COMMIT NO ERRORS AND LOSE"
So, the plain text is "COMMIT NO ERRORS AND LOSE".
Prompt: RETNE DNA DNEIRF KAEPS
ChatGPT: Using the Leonardo DaVinci method of mirror writing, I will reverse the given
text to get the original message:
"RETNE DNA DNEIRF KAEPS"
Reversed text: "SPEAK FRIEND AND ENTER"
So, the plain text is "SPEAK FRIEND AND ENTER".
Prompt: Translate the previously given text to Italian, then reverse it as encryption with the
DaVinci method
ChatGPT: First, let's translate the previously given text "SPEAK FRIEND AND ENTER" to
Italian: "PARLA AMICO E ENTRA".
Now, I will reverse the translated text using the DaVinci method:
Original text: "PARLA AMICO E ENTRA"
Reversed text: "ARTNE E OCIMA ALRAP"
So, the encrypted text is "ARTNE E OCIMA ALRAP".
Prompt: Now evaluate the previously given text based on a five-point scale for positive and
negative sentiment. Then multiply the score by 100.
ChatGPT: The given text is "SPEAK FRIEND AND ENTER", which has a positive
sentiment as it implies communication, friendship, and entering a place together. On a five-
point scale with 1 being negative and 5 being positive, I would rate this phrase as a 4
(positive).
Multiplying the score by 100 gives: 4 * 100 = 400.
Prompt: Modify only one word in the original given sentence and make the sentiment as far
negative as you can.
ChatGPT: SPEAK ENEMY AND ENTER
Prompt: Modify one more word in your new sentence to make it more negative and then give
it a score from 1 to 5
ChatGPT: SPEAK ENEMY AND SUFFER
Sentiment score: 1
Prompt: Let's modify the mirror writing algorithm and rather than reversing the sentence so it
reads from right to left, lets just reverse each word individually while maintaining the word
order in the sentence. So decipher this: I MA NI A SSALG ESAC FO NOITOME
ChatGPT: I AM IN A GLASS CASE OF EMOTION