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

Key points

• Although attributional models are well-studied in social psychology and cognition through Attribution Theory, social attribution remains underexplored in AI, particularly LLM social reasoning.1
• This paper provides the first systematic exploration of LLM social attribution.1
• Guided by attribution theory, we construct a social attribution benchmark consisting of a Vignette subset based on classic scenarios from attribution theory research and a Reality subset based on real-world social narratives, yielding 7,639 responsibility/blame judgment questions.1
• To further explore the internal mechanisms underlying the LLM judgment process, we develop a probing-based methodology to investigate the latent-space representations of 5 key attribution dimensions and the consistency of their influences on LLM judgments compared to those in human social attribution.1

Sources (1)

• [1]Examining Social Attribution in LLM Reasoning: A Theory-Guided Probing Methodology
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 02:20 PM

  Large language models (LLMs) are increasingly deployed in sociotechnical systems where social attribution, the reasoning process attributing external events to the causes and reasons of agents' social behaviors, plays a critical role.

  Although attributional models are well-studied in social psychology and cognition through Attribution Theory, social attribution remains underexplored in AI, particularly LLM social reasoning.

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