用多智能体框架更准地从人生叙事中识别五大性格特质
Fine-Tuned Multi-Agent Framework for Detecting OCEAN in Life Narratives
- 多个子智能体分别从高、低、中立视角分析性格，避免单一模型偏见
- 在真实人生故事数据集上，预测准确率显著优于基线方法
- 适合心理学研究与可解释性要求高的性格分析场景
从文本中准确评估人格特质极具挑战性，因特质隐含、依赖上下文且常以微妙方式表达于长篇叙述中。大型语言模型（LLMs）通过处理广泛文本上下文提供了新机遇，但其预训练过程可能引入潜在的“类人格”偏差，导致单模型推断不一致。本文提出一种微调的多智能体框架，用于检测OCEAN人格特质。各子智能体通过掩码语言建模（MLM）和心理测量监督，被引导采取每项特质的高、低或中立视角。一个裁判型大模型聚合并比较子智能体输出，生成最终特质预测，从而捕捉多重互补视角并缓解个体模型偏差。我们在人生叙事数据集上进行了定量与定性实验，包括基线对比、消融研究及推理质量分析。该方法提供了一种可扩展且可解释的文本人格推断方案，凸显了基于心理测量监督的多智能体推理的优势。
原文摘要 · Abstract (English)
Accurately assessing personality from text is challenging because traits are latent, context-dependent, and often subtly expressed across long narratives. Large language models (LLMs) offer new opportunities by processing extensive textual contexts, but pretraining of these models can induce latent "personality-like" biases, making single-model inferences inconsistent. We propose a fine-tuned multi-agent framework for detecting OCEAN personality traits, in which sub-agents are conditioned to adopt high, low, or neutral perspectives for each trait through masked language modeling (MLM) and psychometric supervision. A judge LLM aggregates and compares sub-agent outputs to generate final trait predictions, capturing multiple complementary perspectives while mitigating individual model biases. We evaluate the framework on life narrative dataset through quantitative and qualitative experiments, including baselines, ablations, and inference quality analyses. Our approach offers a scalable and interpretable method for text-based personality inference, highlighting the benefits of multi-agent reasoning grounded in psychometric supervision.
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