[Submitted on 13 Oct 2026]
Title:From Copying to Composition: How Language Models Develop In-Context Learning
View a PDF of the paper titled From Copying to Composition: How Language Models Develop In-Context Learning, by Minglai Yang and 5 other authors
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Abstract:In-context learning (ICL) allows language models to adapt their behavior from instructions and demonstrations in a prompt, but accuracy alone does not reveal how contextual information controls their predictions. We study the acquisition and trainability of ICL with a controlled diagnostic that separates copying a nearby demonstration from integrating information across the prompt. Across 17 models, about a third prefer the copied answer, a few are undecided, and the rest prefer the rule-consistent answer, which goes with higher accuracy and with control by the requested value rather than the closest conflicting example. Checkpoint analyses show that models begin prioritizing task-relevant prompt tokens over conflicting examples long before their accuracy improves beyond chance level. In matched training experiments, synthetic documents that define endings and then query them shift models toward compositional responses on the controlled diagnostic, whereas fine-tuning on definition exercises built from rewritten real documents improves accuracy when prompts reassign label meanings. The results suggest that ICL is not acquired as a single, uniform capability. Instead, prompt control, answer preference, construction without candidates, and transfer can develop along distinct trajectories.
Comments: 29 pages, 17 figures, 8 tables
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2610.19113 [cs.CL]
(or arXiv:2610.19113v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.19113
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Minglai Yang [view email] [v1] Tue, 13 Oct 2026 19:49:33 UTC (859 KB)
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