Beyond Self-Reports: Multi-Observer Agents for Personality Assessment in Large Language Models

• URL: http://arxiv.org/abs/2504.08399v1
• Date: Fri, 11 Apr 2025 10:03:55 GMT
• Title: Beyond Self-Reports: Multi-Observer Agents for Personality Assessment in Large Language Models
• Authors: Yin Jou Huang, Rafik Hadfi,
• Abstract summary: This paper introduces a novel multi-observer framework for Large language models (LLMs) personality assessment.<n>Instead of relying solely on self-assessments, our approach employs multiple observer agents configured with a specific relationship context.<n>Our experiments reveal that LLMs possess systematic biases in self-report personality ratings.
• Score: 2.7010154811483167
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: There is a growing interest in assessing the personality traits of Large language models (LLMs). However, traditional personality assessments based on self-report questionnaires may fail to capture their true behavioral nuances due to inherent biases and meta-knowledge contamination. This paper introduces a novel multi-observer framework for LLM personality assessment that draws inspiration from informant-report methods in psychology. Instead of relying solely on self-assessments, our approach employs multiple observer agents configured with a specific relationship context (e.g., family, friend, or workplace) to simulate interactive scenarios with a subject LLM. These observers engage in dialogues and subsequently provide ratings across the Big Five personality dimensions. Our experiments reveal that LLMs possess systematic biases in self-report personality ratings. Moreover, aggregating observer ratings effectively reduces non-systematic biases and achieves optimal reliability with 5-7 observers. The findings highlight the significant impact of relationship context on personality perception and demonstrate that a multi-observer paradigm yields a more robust and context-sensitive evaluation of LLM personality traits.

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