A Data Generation Perspective to the Mechanism of In-Context Learning

• URL: http://arxiv.org/abs/2402.02212v2
• Date: Fri, 16 Aug 2024 01:16:20 GMT
• Title: A Data Generation Perspective to the Mechanism of In-Context Learning
• Authors: Haitao Mao, Guangliang Liu, Yao Ma, Rongrong Wang, Kristen Johnson, Jiliang Tang,
• Abstract summary: In-Context Learning (ICL) empowers Large Language Models (LLMs) with the capacity to learn in context. Despite the encouraging empirical success, the underlying mechanism of ICL remains unclear.
• Score: 37.933016939520684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In-Context Learning (ICL) empowers Large Language Models (LLMs) with the capacity to learn in context, achieving downstream generalization without gradient updates but with a few in-context examples. Despite the encouraging empirical success, the underlying mechanism of ICL remains unclear, and existing research offers various viewpoints of understanding. These studies propose intuition-driven and ad-hoc technical solutions for interpreting ICL, illustrating an ambiguous road map. In this paper, we leverage a data generation perspective to reinterpret recent efforts and demonstrate the potential broader usage of popular technical solutions, approaching a systematic angle. For a conceptual definition, we rigorously adopt the terms of skill learning and skill recognition. The difference between them is skill learning can learn new data generation functions from in-context data. We also provide a comprehensive study on the merits and weaknesses of different solutions, and highlight the uniformity among them given the perspective of data generation, establishing a technical foundation for future research to incorporate the strengths of different lines of research.

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