Yao Fu, University of Edinburgh, yao.fu@ed.ac.uk
**Update**: How does GPT Obtain its Ability? Tracing Emergent Abilities of Language Models to their Sources
**Update**: A Closer Look at Language Model Emergent Abilities
**Update**: Reasoning with large language models
Although seemingly trivial and being easily used everyday, our observation and knowledge of human language is restricted, biased and ultimately finite. Yet the variant of human language is at least combinatorially large, and potentially exponential or even infinite. How can we generalize to such large space with such limited observation? Are the current models capable enough to generalize on unseen linguistic scenarios? If one feed all possible data on the internet to a gigantic language model, can it learn everything of human language? If not, what is (still) missed? These are the problems that we would like to study through the lens of generalization.
- Introduction
- Table of Content
- Development of Theory
- Foundations
- Classical Theory
- Deep Learning Theory
- Reasoning with Large Language Models
- Transfer Setting
- Generalization in Natural Language Processing
- Optimization
- Practical Techniques
| Theory | Model Scale | #Param | Data |
|---|---|---|---|
| Classical Supervised Learning Theory | Small model (e.g., SVMs) | <1M | i.i.d. data |
| Deep Supervised Learning Theory | Small neural networks | < 1M | i.i.d. data |
| Generalization in Transfer Setting | Mid neural networks | < 1G | non-i.i.d. data, structures within data, data distributional shift |
| Compositional Generalization in NLP | Pretrained language model | < 11G | non-i.i.d. data, linguistic structures within data, domain transfer, task transfer, cross-lingual transfer |
| Emergent Abilities in Large Language Models | Largest models so far (GPT-3, PaLM, .etc) | > 100G | i.i.d. or non-i.i.d., Few-shot in-context learning |
- Classical Supervised Learning Theory
- Small model (e.g., SVMs), i.i.d. data
- PAC, Rademancher Complexity, VC Dimension
- Deep Supervised Learning Theory
- Large model, neural networks, i.i.d. data
- Over-parameterization, Regularization, Non-convex Optimization, Neural Tangent Kernel
- Generalization in Transfer Setting
- Large model, neural networks, non-i.i.d. data
- Distribution Shift, Domain Adaptation, Robustness, Invariance
- Compositional Generalization in NLP
- Even Larger model (pretrained language model), non-i.i.d., but with intrinsic structures, data
- Semantic Parsing, Question Answering, Language Generation
- Emergent Abilities in Large Language Models
- Largest models so far (GPT-3, PaLM), few-shots in-context learning, reasoning with rationales
- Practical Techniques
- Datasets, Data Augmentation, Architecture Design, Distributionally Robust Optimization, .etc.
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