Summary of An Information-theoretic Analysis Of In-context Learning, by Hong Jun Jeon et al.
An Information-Theoretic Analysis of In-Context Learning
by Hong Jun Jeon, Jason D. Lee, Qi Lei, Benjamin Van Roy
First submitted to arxiv on: 28 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper challenges previous meta-learning research on sequences by introducing new information-theoretic tools that provide a unified framework for analyzing various meta-learning challenges. The authors decompose the error into three components: irreducible, meta-learning, and intra-task errors, allowing them to establish new results about in-context learning with transformers. Their theoretical findings characterize how error decays as the number of training sequences and sequence lengths increase. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to understand how machine learning models learn from multiple examples. It shows that there are three types of mistakes that can happen when learning: some are unavoidable, some are due to not fully understanding the current task, and others are caused by not being able to generalize well enough. The authors use these insights to improve our understanding of how transformers learn in different situations. |
Keywords
* Artificial intelligence * Machine learning * Meta learning
