Is In-Context Learning Learning?

• URL: http://arxiv.org/abs/2509.10414v2
• Date: Mon, 15 Sep 2025 15:29:49 GMT
• Title: Is In-Context Learning Learning?
• Authors: Adrian de Wynter,
• Abstract summary: In-context learning (ICL) allows some autoregressive models to solve tasks via next-token prediction and without needing further training.<n>We argue that mathematically, ICL does constitute learning, but its full characterisation requires empirical work.<n>We find that ICL is an effective learning paradigm, but limited in its ability to learn and generalise to unseen tasks.
• Score: 12.037650994342664
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In-context learning (ICL) allows some autoregressive models to solve tasks via next-token prediction and without needing further training. This has led to claims about these model's ability to solve (learn) unseen tasks with only a few shots (exemplars) in the prompt. However, deduction does not always imply learning, as ICL does not explicitly encode a given observation. Instead, the models rely on their prior knowledge and the exemplars given, if any. We argue that, mathematically, ICL does constitute learning, but its full characterisation requires empirical work. We then carry out a large-scale analysis of ICL ablating out or accounting for memorisation, pretraining, distributional shifts, and prompting style and phrasing. We find that ICL is an effective learning paradigm, but limited in its ability to learn and generalise to unseen tasks. We note that, in the limit where exemplars become more numerous, accuracy is insensitive to exemplar distribution, model, prompt style, and the input's linguistic features. Instead, it deduces patterns from regularities in the prompt, which leads to distributional sensitivity, especially in prompting styles such as chain-of-thought. Given the varied accuracies on formally similar tasks, we conclude that autoregression's ad-hoc encoding is not a robust mechanism, and suggests limited all-purpose generalisability.

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