@inproceedings{mao-etal-2025-survey,
title = "A Survey to Recent Progress Towards Understanding In-Context Learning",
author = "Mao, Haitao and
Liu, Guangliang and
Ma, Yao and
Wang, Rongrong and
Johnson, Kristen and
Tang, Jiliang",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.408/",
doi = "10.18653/v1/2025.findings-naacl.408",
pages = "7302--7323",
ISBN = "979-8-89176-195-7",
abstract = "In-Context Learning (ICL) empowers Large Language Models (LLMs) with the ability to learn from a few examples provided in the prompt, enabling downstream generalization without the requirement for gradient updates. Despite encouragingly empirical success, the underlying mechanism of ICL remains unclear. Existing research remains ambiguous with various viewpoints, utilizing intuition-driven and ad-hoc technical solutions to interpret ICL. In this paper, we leverage a data generation perspective to reinterpret recent efforts from a systematic angle, demonstrating the potential broader usage of these popular technical solutions. For a conceptual definition, we rigorously adopt the terms of skill recognition and skill learning. Skill recognition selects one learned data generation function previously seen during pre-training while skill learning can learn new data generation functions from in-context data. Furthermore, we provide insights into the strengths and weaknesses of both abilities, emphasizing their commonalities through the perspective of data generation. This analysis suggests potential directions for future research. The corresponding paper list can be found here."
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%0 Conference Proceedings
%T A Survey to Recent Progress Towards Understanding In-Context Learning
%A Mao, Haitao
%A Liu, Guangliang
%A Ma, Yao
%A Wang, Rongrong
%A Johnson, Kristen
%A Tang, Jiliang
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F mao-etal-2025-survey
%X In-Context Learning (ICL) empowers Large Language Models (LLMs) with the ability to learn from a few examples provided in the prompt, enabling downstream generalization without the requirement for gradient updates. Despite encouragingly empirical success, the underlying mechanism of ICL remains unclear. Existing research remains ambiguous with various viewpoints, utilizing intuition-driven and ad-hoc technical solutions to interpret ICL. In this paper, we leverage a data generation perspective to reinterpret recent efforts from a systematic angle, demonstrating the potential broader usage of these popular technical solutions. For a conceptual definition, we rigorously adopt the terms of skill recognition and skill learning. Skill recognition selects one learned data generation function previously seen during pre-training while skill learning can learn new data generation functions from in-context data. Furthermore, we provide insights into the strengths and weaknesses of both abilities, emphasizing their commonalities through the perspective of data generation. This analysis suggests potential directions for future research. The corresponding paper list can be found here.
%R 10.18653/v1/2025.findings-naacl.408
%U https://aclanthology.org/2025.findings-naacl.408/
%U https://doi.org/10.18653/v1/2025.findings-naacl.408
%P 7302-7323
Markdown (Informal)
[A Survey to Recent Progress Towards Understanding In-Context Learning](https://aclanthology.org/2025.findings-naacl.408/) (Mao et al., Findings 2025)
ACL