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Beyond the Symbols: A 30-minute Overview of NLP
- 1. Beyond the $!mb0ls A 30-minute Overview of Natural Language Processing Mengsay Loem Tokyo Insitute of Technology 2023/06/17
- 3. What is NLP? Natural Language Processing • Processing of natural languages used by humans • e.g., English, Japanese, Khmer, Chinese … • Making natural language understandable to machines 3 English ⽇本語 !"ែខ%រ C/C++, Java Python, R Lojban Natural Language Artificial Language Natural Language Processing
- 4. Some Tasks in NLP 4 Recent advances in dept learning-based optimization and computational hardware have greatly facilitated progress in natural language processing 近年のディープラーニングに 基づく最適化と計算機ハード ウェアの進歩は、⾃然⾔語処 理の進展を⼤きく促している ⾃然⾔語処理の進展促す ＝ディープラーニング最適化 と計算機ハードウェア Recent advances in dept learning-based optimization and computational hardware has greatly facilitated progress in naturail language processing Machine translation Information Retrieval Proofreading/ Error Correction Document classification/clustering Dialogue System Proofreading/ Error Correction Translation Summarization
- 5. Difficulties in NLP Back to Basic of Languages 5
- 6. What are Languages for? 6 Tool for Communication Tool for Thought Tool for Record
- 7. Some Features of Languages 7 Arbitrariness Connection between a word and its meaning is often arbitrary Sociolinguistics Many aspects of language usage are based on social customs and can't always be logically explained Evolving Language changes over time and varies across regions and cultures Networked Language reflects complex relationships between entities and concepts Ambiguous A single expression can have multiple meanings based on context
- 8. Some Features of Languages 8 Arbitrariness Connection between a word and its meaning is often arbitrary Sociolinguistics Many aspects of language usage are based on social customs and can't always be logically explained Evolving Language changes over time and varies across regions and cultures Networked Language reflects complex relationships between entities and concepts Ambiguous A single expression can have multiple meanings based on context Computer must have vast knowledge of languages Computers must be flexible in interpreting meaning in text
- 9. Some Difficulties in NLP • Ambiguity in Semantics • sleep = 寝る • Natural Language Processing = NLP • … machine learning… ; … car machine … • Dealing with hierarchical sequential data • character → word → phrase → sentence → paragraph → text • What does language comprehension entail for machine? • To machine, input text is just a sequence to SYMBOLs 9
- 10. Approaches to NLP From Rule-based to Deep Learning-based 10
- 11. Before vs. After Deep Learning 11 Input text POS Tagging Syntatic Parsing Predicate-Argument Recognition Application System Output Input text Output Training Data Deep Learning based NLP Traditional NLP Training Data Neural Network
- 12. NLP’s Methods in a Century • 1940s~1960s • First Computer: ENIAC (1946) • Translating Machine Project (1952) • Limit of Computing Performance • 1960s~1990s • Digital text data; Brown Corpus (1967) • MEDLINE Database Service (1971) • Manual-Rule-based Text Analysis • 1990s~2010s (※1990: WWW, 1998: Google) • Large-scale Corpora + Machine Learning • Translation by Analogy (京大1981) • Statistical Machine Translation (1980s) • CALO (origin of Siri), Watson 12 • 2010s • Mainly on Neural Network • Word2Vec (Google, 2013) • Neural Machine Translation (2014) • Transformer (Google, 2017) • Pre-trained Language Models, BERT (2018), GPT-2 (2019), BART(2019) • 2020s • Large-scaled Pre-trained Language Models (LLMs) • GPT-3 (2020), T5 (2020), ChatGPT (2022), GPT-4(2023)
- 13. Neural Network + NLP Basic of Neural Network and How it works in NLP 13
- 14. Basic structure of Neural Network • Linear and Non-linear Transformation • Matrix Multiplication＋Non-linear Activation Function • ! = # ∑% &%'% 14 '( ') 0.3 −0.5 0.1 1 1 + 123 0.76 0.4 ∑ '7 '8
- 15. Basic structure of Neural Network • Linear and Non-linear Transformation • Matrix Multiplication＋Non-linear Activation Function • ! = # ∑% &%'% • Input: adjective and noun in a movie review • Output: 0 (negative) / 1 (positive) • Such a wonderful movie. → 1 (positive) 15 movie boring wonderful 0.3 −2.5 3.1 1 1 + 012 0.97 time 0.1 ∑
- 16. Basic structure of Neural Network • Linear and Non-linear Transformation • Matrix Multiplication＋Non-linear Activation Function • ! = # ∑% &%'% • Input: adjective and noun in a movie review • Output: 0 (negative) / 1 (positive) • Boring time of the year. → 0 (negative) 16 movie boring wonderful 0.3 −2.5 3.1 1 1 + 012 0.08 time 0.1 ∑
- 17. Basic structure of Neural Network • Linear and Non-linear Transformation • Matrix Multiplication＋Non-linear Activation Function • ! = # ∑% &%'% • Input: adjective and noun in a movie review • Output: 0 (negative) / 1 (positive) • Boring time of the year. → 0 (negative) 17 movie boring wonderful 0.3 −2.5 3.1 1 1 + 012 0.08 time 0.1 ∑ How to represent these words?
- 18. Neural Network for NLP • How to represent input word/sentence/text as a vector ? • Simple solution: one-hot vector for word level • Bag-of-words: each element represents occurrence frequency 18 0 0 1 ⋮ 0 0 1 0 0 ⋮ 0 0 movie great like ⋮ love I like movie 1 0 1 ⋮ 0 1 I like movie vocabulary size (10K - 100K)
- 19. Neural Network for NLP • How to represent input word/sentence/text as a vector ? • Simple solution: one-hot vector for word level • Bag-of-words: each element represents occurrence frequency • Problems: • Cannot deal with Synonymy • Cannot deal with differences in word orders • Distributional hypothesis words in the same contexts tend to be sematically similar 19 1 0 0 ⋮ 0 0 like 0 0 0 ⋮ 1 0 love
- 20. Embedding Representation • Represent a word by a real number vector • Share features of each word in embedding space (vector space) • Represent similar words with similar vectors • (use lower dimension compared to Bag-of-words) 20 0.12 −1.90 ⋮ 0.55 1.37 book dictionary 0.17 −1.80 ⋮ 0.52 1.57 phone 0.97 0.10 ⋮ 1.63 −0.11 Similar Not Similar Dense vector 100 - 1K dimensions book dictionary phone Defined by Neural Network
- 21. word2vec • Learn to represent a word by an embedding vector • CBoW, skip-gram • Example: • Start with randomly initialized embedding vectors • Learn to predict a target word given its contexts • Maximize target word’s probability 21 …… he ate an apple yesterday with …… !"#$ !"#% !" !"&% !"&$ ' "(% ) ' #*+,+*,,./ log 3 !"|!"&,
- 22. Neural Network for NLP • How to represent input word/sentence/text as a vector ? • Simple solution: one-hot vector for word level • Bag-of-words: each element represents occurrence frequency • Problems: • Cannot deal with Synonymy → Embedding representation • Cannot deal with differences in word orders • Distributional hypothesis words in the same contexts tend to be sematically similar 22 1.67 0.45 −1.80 0.12 like 1.57 0.65 −1.60 0.15 love
- 23. Neural Network for NLP • How to represent input word/sentence/text as a vector ? • Simple solution: one-hot vector for word level • Bag-of-words: each element represents occurrence frequency • Problems: • Cannot deal with Synonymy → Embedding representation • Cannot deal with differences in word orders → revise network architecture • Distributional hypothesis words in the same contexts tend to be sematically similar 23
- 24. Dealing with Sequential Data • Recurrent Neural Network • Related architectures • Long Short-Term Memory (LSTM) • Gated Recurrent Unit (GRU) • Transformer • Attention mechanism • Basic Neural Network architecture (Feed-Forward Network) 24
- 25. Solving NLP Tasks: Examples • Task: • Machine Translation (MT) : English → Khmer • Summarization : Document → Summary • Grammatical Error Correction • Model: • Encoder-Decoder • Data • Parallel data {(input, output)} 25 Encoder Decoder Hidden Representation Input sequence Output sequence RNN, LSTM, Transformer,…
- 26. LLMs Era of NLP What is happening with Large-scale Language Models? 26
- 28. Solving NLP Tasks with LLMs • Pre-train & Fine-tune Paradigm • Pre-train a language model with large corpora • Fine-tune pre-trained model on specific task with (small) data sets • Prompt-based Method • Pre-train a language model with large corpora • Ask model to solve various tasks with prompt written in natural language 28
- 29. Pre-train & Fine-tune Paradigm • Train a Language Model with (very very) large data sets • Fine-tune pre-trained model on (specific) target tasks • Document Classification, Machine Translation, Summarization… 29 機械学習（きかいがくしゅう、英: machine learning）とは、経験からの学習により⾃ 動で改善するコンピューターアルゴリズム もしくはその研究領域で[1][2]、⼈⼯知能 の⼀種であるとみなされている。「訓練 データ」もしくは「学習データ」と呼ばれ るデータを使って学習し、学習結果を使っ て何らかのタスクをこなす。例えば過去の スパムメールを訓練データとして⽤いて学 習し、スパムフィルタリングというタスク をこなす、といった事が可能となる。 Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data.[1] It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.[2] Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.[3] Fine-tuning Recent advances in deep learning-based optimization and computational hardware have greatly facilitated progress in natural language processing 近年のディープラーニングに基づく最適化と計算機ハードウェ アの進歩は、⾃然⾔語処理の進展を⼤きく促している Pre-training I have a GPT-n pen I [MASK] a pen BERT have I [MASK] pen a BART I have a pen
- 30. Pre-trained Model • Why pre-train on Language Model ? • Large scale training data is available !!! • Neural-based model is Data Hungry! 30 Pre-training I have a GPT-n pen I [MASK] a pen BERT have I [MASK] pen a BART I have a pen
- 32. How ChatGPT works? From next word prediction to human-instructed training 32
- 33. (Probabilistic) Language Model • Models that assign probabilities to sequence of words • To evaluate text (sequence) generated by a system • ! I, like, watching, movie > ! I, eat, watch, movie • Predict next coming word • Many NLP tasks can be formulated as language modeling 33 The capital of Japan is Beijing Seattle Tokyo 0.20 0.05 0.75 ! Tokyo | The, capital, of, Japan, is = 0.75 ! The, capital, of, Japan, is, Tokyo, ja , 日本, の, 首都, は, A
- 34. Training with Human Feedback 34 L Ouyang, J Wu, X Jiang, D Almeida, et. al. 2022. Training Language Models to Follow Instructions with Human Feedback. arXiv:2203.02155.
- 35. Improve LLMs with Discussions • Solving NLP Problems through Human-System Collaboration: A Discussion-based Approach • Kaneko et al. 2023 35
- 36. Beyond Performance Risks and Problems to Solve in LLMs Era 36
- 37. Threats to LLMs • Turning point in a wide range of fields • including search engine, finance, advertising, education, and legal. • There's been an explosive increase in services incorporating LLMs. • Jobs such as translators, investigators, writers, etc. are being shortened (Eloundou+2023). • Hallucinations • Even when unsure, they can calmly lie, responding without basis in fact. • Bias • They learn and amplify societal biases related to gender, race, etc. • They can be adjusted to respond in ways that benefit specific individuals or groups. • Personal information exposure • Misuse 37 - T Eloundou, S Manning, P Mishkin, D Rock. 2023. GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. arXiv:2303.10130. - 岡崎 直観 2023. ⼤規模⾔語モデルの驚異と脅威. https://speakerdeck.com/chokkan/20230327_riken_llm
- 38. 38
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- 40. 40 Data and GPUs are All You Need!
- 41. Summary • NLP aims to make human’s language understandable to machines. • Many difficulties for NLP due to languages’ characteristics, such as ambiguity, complex network structures, etc. • Deep Learning-based methods have been pushing NLP to an impressive achievement over these decades. • New era of NLP has been coming with large-scale language models • There are still many problems to deal with NLP • Low-resource languages • Bias, Hallucination, etc. 41