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arXiv Computation and Language By Xiang Fu, Seungmin Cho, Yukyung Lee, Najoung Kim

LLMs Learn Better In-Context from Rules than from Examples

Read the original on arXiv Computation and Language →

The paper investigates how large language models learn new tasks in-context, comparing rule-based instruction following to example-based few-shot prompting across five diverse tasks. Results show that models generally learn more reliably from rule descriptions than from examples alone, and adding more examples does not consistently improve performance. Instruction tuning further enhances rule-based learning while preserving example-based capabilities, with rule advantages being strongest for algebraic tasks and weaker for tasks requiring distributional sensitivity or parametric knowledge.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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