Papers › Revisiting In-context Learning Inference Circuit in Large Language Models

Revisiting In-context Learning Inference Circuit in Large Language Models

6 Oct 2024arXiv:2410.04468archive 2025-07-28

Hakaze Cho, Mariko Kato, Yoshihiro Sakai, Naoya Inoue

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) Input Text Encode: 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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ICL_inference_to_hidden_states hc495/ICL_Circuit/util/inference.py official repository ran MIT (permissive) · 6d2ba9201e0266c1 · report
ICL_inference_to_hidden_states_transposed hc495/ICL_Circuit/util/inference.py official repository ran MIT (permissive) · 0e4a9d80be097eab · report
encoder_inference_to_feature hc495/ICL_Circuit/util/inference.py official repository ran MIT (permissive) · 03f5ae8eefe2ed8f · report
find_tokenized_label_word hc495/ICL_Circuit/util/ablation_study.py official repository ran MIT (permissive) · dc2a498623d25b35 · report
get_induction_correctness_for_single_layer hc495/ICL_Circuit/util/induction_evaluation.py official repository ran MIT (permissive) · ab0facc671844bd0 · report
get_induction_magnitude_for_single_layer hc495/ICL_Circuit/util/induction_evaluation.py official repository ran MIT (permissive) · 36830c7e7fd81df1 · report
kernel_alignment hc495/ICL_Circuit/util/kernel_alignment.py official repository ran MIT (permissive) · 61a2e6dfc5a9fe1f · report
load_data_from_StaICC_experimentor hc495/ICL_Circuit/util/load_model_and_data.py official repository ran MIT (permissive) · 638cfdf35bdeb6ba · report
overlap hc495/ICL_Circuit/util/kernel_alignment.py official repository ran fingerprinted MIT (permissive) · 5d93a88b24afc491 · report
sim_graph hc495/ICL_Circuit/util/kernel_alignment.py official repository ran MIT (permissive) · 81967b2af6f55803 · report
tokenized_length hc495/ICL_Circuit/util/induction_evaluation.py official repository ran MIT (permissive) · 3a45ae8b2ab1d6e9 · report
Masked_ICL_inference hc495/ICL_Circuit/util/ablation_study.py official repository unverified MIT (permissive) · 8ac576595ccbc620 · report
head_ablation hc495/ICL_Circuit/util/ablation_study.py official repository unverified MIT (permissive) · ab89dd08b607b43c · report
load_ICL_model hc495/ICL_Circuit/util/load_model_and_data.py official repository unverified MIT (permissive) · f906d6d0f7a495ad · report
load_encode_model hc495/ICL_Circuit/util/load_model_and_data.py official repository unverified MIT (permissive) · fdf0bd39d2cf5fed · report

Tasks

Few-Shot LearningIn-Context Learning

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