Revisiting In-context Learning Inference Circuit in Large Language Models

• URL: http://arxiv.org/abs/2410.04468v1
• Date: Sun, 6 Oct 2024 12:50:15 GMT
• Title: Revisiting In-context Learning Inference Circuit in Large Language Models
• Authors: Hakaze Cho, Mariko Kato, Yoshihiro Sakai, Naoya Inoue,
• Abstract summary: In-context learning (ICL) is an emerging few-shot learning paradigm on Language Models (LMs) with inner mechanisms un-explored. This paper proposes a comprehensive circuit to model the inference dynamics and try to explain the observed phenomena of ICL.
• Score: 2.4866936275046405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In-context Learning (ICL) is an emerging few-shot learning paradigm on Language Models (LMs) with inner mechanisms un-explored. There are already existing works describing the inner processing of ICL, while they struggle to capture all the inference phenomena in large language models. Therefore, this paper proposes a comprehensive circuit to model the inference dynamics and try to explain the observed phenomena of ICL. In detail, we divide ICL inference into 3 major operations: (1) Summarize: LMs encode every input text (demonstrations and queries) into linear representation in the hidden states with sufficient information to solve ICL tasks. (2) Semantics Merge: LMs merge the encoded representations of demonstrations with their corresponding label tokens to produce joint representations of labels and demonstrations. (3) Feature Retrieval and Copy: LMs search the joint representations similar to the query representation on a task subspace, and copy the searched representations into the query. Then, language model heads capture these copied label representations to a certain extent and decode them into predicted labels. The proposed inference circuit successfully captured many phenomena observed during the ICL process, making it a comprehensive and practical explanation of the ICL inference process. Moreover, ablation analysis by disabling the proposed steps seriously damages the ICL performance, suggesting the proposed inference circuit is a dominating mechanism. Additionally, we confirm and list some bypass mechanisms that solve ICL tasks in parallel with the proposed circuit.

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