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arXiv AI By Kohsei Matsutani, Gouki Minegishi, Core Francisco Park, Takeshi Kojima, Yusuke Iwasawa, Yutaka Matsuo

Few-Shot Demonstrations Elicit the Use of In-Context World Representations in LLMs

Read the original on arXiv AI →

The paper investigates how few-shot demonstrations help large language models (LLMs) build and use in-context world representations. By providing demonstrations from different worlds, the authors show that LLMs can better encode graph structures in low-dimensional hidden states, leading to improved predictions across six models from four families. The study also demonstrates that these representations shift to nearly orthogonal subspaces and that targeted interventions on them more significantly impair performance, indicating their critical role in downstream tasks such as ARC-AGI, web agent tasks, and Othello.

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 AI.

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