Summary of Revisiting In-context Learning Inference Circuit in Large Language Models, by Hakaze Cho et al.
Revisiting In-context Learning Inference Circuit in Large Language Models
by Hakaze Cho, Mariko Kato, Yoshihiro Sakai, Naoya Inoue
First submitted to arxiv on: 6 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a comprehensive circuit to model the inference dynamics of In-Context Learning (ICL), an emerging few-shot learning paradigm on Language Models (LMs). The authors divide ICL inference into three major operations: Input Text Encode, Semantics Merge, and Feature Retrieval and Copy. These operations enable LMs to encode input text, merge label tokens with demonstrations, and retrieve features from the joint representation. The proposed circuit successfully captures various phenomena observed during ICL, making it a practical explanation of the ICL inference process. Evaluation shows that disabling individual steps seriously damages ICL performance, confirming the importance of the proposed circuit. Additionally, the authors identify bypass mechanisms that solve ICL tasks in parallel. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how language models learn new things from small examples. It proposes a way to understand what’s happening inside these models when they’re learning. The approach breaks down into three steps: encoding input text, merging information with labels, and retrieving important features. This helps us understand why the models are making certain predictions. By testing different parts of this process, we see that each step is crucial for the model to learn effectively. |
Keywords
» Artificial intelligence » Few shot » Inference » Semantics
