LLM GPT-3: Language models are few-shot learners
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gpt3_presentation.pdf
- GPT-3 is a large language model developed by OpenAI, with 175 billion parameters, making it the largest neural network ever created at the time.
- GPT-3 is trained on a massive dataset of unlabeled text using an auto-regressive approach, allowing it to perform tasks without any fine-tuning through zero-, one-, or few-shot learning by conditioning on examples or instructions.
- Evaluation showed GPT-3 outperforming state-of-the-art models on several benchmarks in zero- and few-shot settings, demonstrating strong generalization abilities from its massive pre-training.
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gpt3_presentation.pdf
- GPT-3 is a large language model developed by OpenAI, with 175 billion parameters, making it the largest neural network ever created at the time.
- GPT-3 is trained on a massive dataset of unlabeled text using an auto-regressive approach, allowing it to perform tasks without any fine-tuning through zero-, one-, or few-shot learning by conditioning on examples or instructions.
- Evaluation showed GPT-3 outperforming state-of-the-art models on several benchmarks in zero- and few-shot settings, demonstrating strong generalization abilities from its massive pre-training.
Roman Kyslyi: Великі мовні моделі: огляд, виклики та рішення
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GPT-3 is a large language model trained by OpenAI to be task agnostic. It has 175 billion parameters compared to its predecessor GPT-2 which has 1.5 billion parameters. OpenAI plans to provide API access to select partners to query GPT-3 rather than releasing the full model. This could accelerate the development of NLP applications and allow startups to build minimum viable products without training their own models if GPT-3 performance is good enough. However, startups relying solely on the API may lack expertise to improve upon initial products.
AN IMPROVED MT5 MODEL FOR CHINESE TEXT SUMMARY GENERATION
Complicated policy texts require a lot of effort to read, so there is a need for intelligent interpretation of
Chinese policies. To better solve the Chinese Text Summarization task, this paper utilized the mT5 model
as the core framework and initial weights. Additionally, In addition, this paper reduced the model size
through parameter clipping, used the Gap Sentence Generation (GSG) method as unsupervised method,
and improved the Chinese tokenizer. After training on a meticulously processed 30GB Chinese training
corpus, the paper developed the enhanced mT5-GSG model. Then, when fine-tuning the Chinese Policy
text, this paper chose the idea of “Dropout Twice”, and innovatively combined the probability distribution
of the two Dropouts through the Wasserstein distance. Experimental results indicate that the proposed
model achieved Rouge-1, Rouge-2, and Rouge-L scores of 56.13%, 45.76%, and 56.41% respectively on
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Complicated policy texts require a lot of effort to read, so there is a need for intelligent interpretation of
Chinese policies. To better solve the Chinese Text Summarization task, this paper utilized the mT5 model
as the core framework and initial weights. Additionally, In addition, this paper reduced the model size
through parameter clipping, used the Gap Sentence Generation (GSG) method as unsupervised method,
and improved the Chinese tokenizer. After training on a meticulously processed 30GB Chinese training
corpus, the paper developed the enhanced mT5-GSG model. Then, when fine-tuning the Chinese Policy
text, this paper chose the idea of “Dropout Twice”, and innovatively combined the probability distribution
of the two Dropouts through the Wasserstein distance. Experimental results indicate that the proposed
model achieved Rouge-1, Rouge-2, and Rouge-L scores of 56.13%, 45.76%, and 56.41% respectively on
the Chinese policy text summarization dataset.
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Complicated policy texts require a lot of effort to read, so there is a need for intelligent interpretation of
Chinese policies. To better solve the Chinese Text Summarization task, this paper utilized the mT5 model
as the core framework and initial weights. Additionally, In addition, this paper reduced the model size
through parameter clipping, used the Gap Sentence Generation (GSG) method as unsupervised method,
and improved the Chinese tokenizer. After training on a meticulously processed 30GB Chinese training
corpus, the paper developed the enhanced mT5-GSG model. Then, when fine-tuning the Chinese Policy
text, this paper chose the idea of “Dropout Twice”, and innovatively combined the probability distribution
of the two Dropouts through the Wasserstein distance. Experimental results indicate that the proposed
model achieved Rouge-1, Rouge-2, and Rouge-L scores of 56.13%, 45.76%, and 56.41% respectively on
the Chinese policy text summarization dataset.
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Complicated policy texts require a lot of effort to read, so there is a need for intelligent interpretation of
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as the core framework and initial weights. Additionally, In addition, this paper reduced the model size
through parameter clipping, used the Gap Sentence Generation (GSG) method as unsupervised method,
and improved the Chinese tokenizer. After training on a meticulously processed 30GB Chinese training
corpus, the paper developed the enhanced mT5-GSG model. Then, when fine-tuning the Chinese Policy
text, this paper chose the idea of “Dropout Twice”, and innovatively combined the probability distribution
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the Chinese policy text summarization dataset.
AN IMPROVED MT5 MODEL FOR CHINESE TEXT SUMMARY GENERATION
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Chinese policies. To better solve the Chinese Text Summarization task, this paper utilized the mT5 model
as the core framework and initial weights. Additionally, In addition, this paper reduced the model size
through parameter clipping, used the Gap Sentence Generation (GSG) method as unsupervised method,
and improved the Chinese tokenizer. After training on a meticulously processed 30GB Chinese training
corpus, the paper developed the enhanced mT5-GSG model. Then, when fine-tuning the Chinese Policy
text, this paper chose the idea of “Dropout Twice”, and innovatively combined the probability distribution
of the two Dropouts through the Wasserstein distance. Experimental results indicate that the proposed
model achieved Rouge-1, Rouge-2, and Rouge-L scores of 56.13%, 45.76%, and 56.41% respectively on
the Chinese policy text summarization dataset.
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Complicated policy texts require a lot of effort to read, so there is a need for intelligent interpretation of
Chinese policies. To better solve the Chinese Text Summarization task, this paper utilized the mT5 model
as the core framework and initial weights. Additionally, In addition, this paper reduced the model size
through parameter clipping, used the Gap Sentence Generation (GSG) method as unsupervised method,
and improved the Chinese tokenizer. After training on a meticulously processed 30GB Chinese training
corpus, the paper developed the enhanced mT5-GSG model. Then, when fine-tuning the Chinese Policy
text, this paper chose the idea of “Dropout Twice”, and innovatively combined the probability distribution
of the two Dropouts through the Wasserstein distance. Experimental results indicate that the proposed
model achieved Rouge-1, Rouge-2, and Rouge-L scores of 56.13%, 45.76%, and 56.41% respectively on
the Chinese policy text summarization dataset.
LLM.pdf
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Processing techniques like TF-IDF, Bag of Words, Latent Dirichlet Allocation, Cosine Similarity along with Ensemble Modelling Techniques like Meta-Bagging with Boosting (XGBoost), that predicts for each gene-mutation pair the most likely category they might fall into. Thus a smart clinical literature reading system can be built to help oncologists quickly identify the clinical relevance of a mutation, and design
the treatment based on that.
Training language models to follow instructions with human feedback (Instruct...
Training language models to follow instructions with human feedback (InstructGPT).pptx
Long Ouyang, Jeff Wu, Xu Jiang et al. (OpenAI)
Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users. In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback. Starting with a set of labeler-written prompts and prompts submitted through the OpenAI API, we collect a dataset of labeler demonstrations of the desired model behavior, which we use to fine-tune GPT-3 using supervised learning. We then collect a dataset of rankings of model outputs, which we use to further fine-tune this supervised model using reinforcement learning from human feedback. We call the resulting models InstructGPT. In human evaluations on our prompt distribution, outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters. Moreover, InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets. Even though InstructGPT still makes simple mistakes, our results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent.
Naver learning to rank question answer pairs using hrde-ltc
The automatic question answering (QA) task has long been considered a primary objective of artificial intelligence.
Among the QA sub-systems, we focused on answer-ranking part. In particular, we investigated a novel neural network architecture with additional data clustering module to improve the performance in ranking answer candidates which are longer than a single sentence. This work can be used not only for the QA ranking task, but also to evaluate the relevance of next utterance with given dialogue generated from the dialogue model.
In this talk, I'll present our research results (NAACL 2018), and also its potential use cases (i.e. fake news detection). Finally, I'll conclude by introducing some issues on previous research, and by introducing recent approach in academic.
Comparative Analysis of Transformer Based Pre-Trained NLP Models
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acomprehensivereviewoflargelanguagemodelsfor-230515063139-1fc27b64.pdf
The document presents a review of large language models (LLMs) for code generation. It discusses different types of LLMs including left-to-right, masked, and encoder-decoder models. Existing models for code generation like Codex, GPT-Neo, GPT-J, and CodeParrot are compared. A new model called PolyCoder with 2.7 billion parameters trained on 12 programming languages is introduced. Evaluation results show PolyCoder performs less well than comparably sized models but outperforms others on C language tasks. In general, performance improves with larger models and longer training, but training solely on code can be sufficient or advantageous for some languages.
A Comprehensive Review of Large Language Models for.pptx
The document presents a review of large language models (LLMs) for code generation. It discusses different types of LLMs including left-to-right, masked, and encoder-decoder models. Existing models for code generation like Codex, GPT-Neo, GPT-J, and CodeParrot are compared. A new model called PolyCoder with 2.7 billion parameters trained on 12 programming languages is introduced. Evaluation results show PolyCoder performs less well than comparably sized models but outperforms others on C language tasks. In general, performance improves with larger models and longer training, but training solely on code can be sufficient or advantageous for some languages.
Gpt1 and 2 model review
The document summarizes three papers on language models: GPT-1, GPT-2, and GPT-3. GPT-1 demonstrated that pre-training a language model on unlabeled text can improve performance on downstream tasks. GPT-2 showed that language models can learn tasks without explicit supervision when trained on a large and diverse dataset. GPT-3 exhibited few-shot learning abilities, achieving strong performance with only a few examples.
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AN IMPROVED MT5 MODEL FOR CHINESE TEXT SUMMARY GENERATION
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NIPS 2017 Competition Track : Personalized Cancer Treatment -- Classifying Cl...
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Naver learning to rank question answer pairs using hrde-ltc
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acomprehensivereviewoflargelanguagemodelsfor-230515063139-1fc27b64.pdf
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
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LLM GPT-3: Language models are few-shot learners
- 1. GPT-3: LANGUAGE MODELS ARE FEW-SHOT LEARNERS LLM Reading Group
- 4. GPT-3 Applications: Building Apps • Demo
- 5. From GPT to GPT-4 • Training language models to follow instructions with human feedback (GPT-3.5/InstructGPT) – over 350B parameters • ChatGPT Release • Large-scale Multimodal model with better post-training alignment (GPT-4) – over 1.5T parameters 06/2017 02/2019 05/2020 03/2022 03/2023 11/2022 Attention Is All You Need 06/2018 Pre-train and Fine-tune Zero-shot In-context few-shot Human Alignment Transformer Architecture Multi-modal
- 6. GPT: Predicting the next token
- 7. GPT-3 Model Architecture • Alternating dense and locally banded sparse attention patterns, similar to the Sparse Transformer. • Layer normalization was moved to the input of each sub-block, and an additional layer normalization was added after the final self-attention block. • We scale the weights of residual layers at initialization by a factor of 1/ √ N where N is the number of residual layers. • The vocabulary is expanded to 50,257. We also increase the context size from 512 to 1024 tokens and a larger batch size of 512 is used. GPT GPT-2 GPT-3
- 8. GPT-3: Increasing model size Compare the model performance across different NLP tasks with an increasing model size.
- 10. Training: Datasets used to train GPT-3
- 11. Evaluation • For few-shot learning, we evaluate each example in the evaluation set by randomly drawing K examples from that task’s training set as conditioning (in-context examples), delimited by 1 or 2 newlines depending on the task. • K can be any value from 0 to the maximum amount allowed by the model’s context window, which is nctx = 2048 for all models and typically fits 10 to 100 examples. Larger values of K are usually but not always better • On tasks with free-form completion, we use beam search with a beam width of 4 and a length penalty of α = 0.6.
- 12. Task Phrasing and Specifications
- 13. commonsense reasoning Language understanding Natural language inference (entailment/contradiction/neutral) From non-English to English
- 18. PIQA: Physical Interaction: Question Answering
- 20. COPA: Choice Of Plausible Alternatives SuperGLUE: Super general language understanding evaluation
- 23. GPT-3 model is biased and tend to reflect stereotypes present in their training data.
- 24. commonsense reasoning Language understanding Natural language inference (entailment/contradiction/neutral) From non-English to English Open-Book QA
- 25. From GPT to GPT-4 • Training language models to follow instructions with human feedback (GPT-3.5/InstructGPT) – over 350B parameters • ChatGPT Release • Large-scale Multimodal model with better post-training alignment (GPT-4) – over 1.5T parameters 06/2017 02/2019 05/2020 03/2022 03/2023 11/2022 Attention Is All You Need 06/2018 Pre-train and Fine-tune Zero-shot In-context few-shot Human Alignment Transformer Architecture Multi-modal More Coming Up!
Editor's Notes
- LayerNorm enables faster training of Transformer and is irreplaceable in this framework. Despite its great success, it is still unclear why LayerNorm is so effective. The widely accepted explanation is that forward normalization brings distribution stability [Ioffe and Szegedy, 2015, Lei Ba et al., 2016].
- models tend to reflect stereotypes present in their training data. Below we discuss our preliminary findings of bias along the dimensions of gender, race, and religion