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New research questions direct link between LLM copying and abstractive learning

By PulseAugur Editorial · [1 sources] ·

A new research paper titled "In-Context Learning Beyond Copying: A Training-Time Analysis of Abstractive ICL" by Kerem Şahin explores the relationship between in-context learning (ICL) capabilities in large language models (LLMs) and the emergence of induction circuits. The study introduces a training regime called Hapax, which reduces the influence of predictable copying during training. Findings suggest that abstractive ICL, where answers are not directly in the provided context, can still develop even with reduced copying and fewer induction heads, challenging previous hypotheses about the direct link between these phenomena. AI

IMPACT Challenges existing hypotheses on how LLMs develop abstractive in-context learning capabilities, potentially influencing future training methodologies.

RANK_REASON Research paper published on arXiv detailing a new training regime for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research questions direct link between LLM copying and abstractive learning

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Research paper published on arXiv detailing a new training regime for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Kerem Sahin, Sheridan Feucht, Adam Belfki, Jannik Brinkmann, Aaron Mueller, David Bau, Chris Wendler ·

    In-Context Learning Beyond Copying: A Training-Time Analysis of Abstractive ICL

    arXiv:2511.05743v3 Announce Type: replace Abstract: The sudden emergence of induction circuits and the ability to perform in-context copying in LLMs coincides with a sharp drop in training loss over a short span of the training process. This co-occurrence has been cited as eviden…

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