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arXiv AI By Zhaoxin Yu, Qingchao Kong, Dajun Zeng, Wenji Mao

Examining Social Attribution in LLM Reasoning: A Theory-Guided Probing Methodology

Read the original on arXiv AI →

The paper introduces a systematic study of how large language models (LLMs) perform social attribution—specifically responsibility and blame judgments—by creating a benchmark with 7,639 questions drawn from classic attribution theory scenarios and real-world narratives. It evaluates 32 LLMs and 5 non-LLM baselines, finding that larger models show greater but still incomplete alignment with human judgments. A probing methodology further reveals that certain attribution dimensions can be decoded from specific hidden-state positions, and their influence on LLM outputs mirrors patterns predicted by human Attribution Theory.

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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