研究职业人格对专业问答的影响，发现特定人格未必提升表现。
Exploring the Impact of Occupational Personas on Domain-Specific QA
- 区分职业型人格与职业性格型人格，测试其对专业问答影响。
- 职业型人格轻微提升准确率，性格型人格反而降低性能。
- 适合关注LLM社会认知建模与领域问答优化的研究者。
近期关于人格的研究提升了大语言模型（LLMs）与用户交互的能力。然而，人格对领域特定问答（QA）任务的影响仍存争议。本研究通过引入两类人格：基于职业的人格（PBPs，如科学家），直接关联领域专长；基于职业性格的人格（OPBPs，如科学型人格），反映认知倾向而非明确专长。在多个科学领域进行实证评估后发现，尽管PBPs能轻微提升准确率，但OPBPs常导致性能下降，即使语义上相关。结果表明，人格相关性本身不足以保证知识有效利用，反而可能施加认知约束，阻碍最优知识应用。未来研究可探索人格表征的细微差异如何引导LLM，或有助于实现更贴近人类社会认知的推理与知识检索。
原文摘要 · Abstract (English)
Recent studies on personas have improved the way Large Language Models (LLMs) interact with users. However, the effect of personas on domain-specific question-answering (QA) tasks remains a subject of debate. This study analyzes whether personas enhance specialized QA performance by introducing two types of persona: Profession-Based Personas (PBPs) (e.g., scientist), which directly relate to domain expertise, and Occupational Personality-Based Personas (OPBPs) (e.g., scientific person), which reflect cognitive tendencies rather than explicit expertise. Through empirical evaluations across multiple scientific domains, we demonstrate that while PBPs can slightly improve accuracy, OPBPs often degrade performance, even when semantically related to the task. Our findings suggest that persona relevance alone does not guarantee effective knowledge utilization and that they may impose cognitive constraints that hinder optimal knowledge application. Future research can explore how nuanced distinctions in persona representations guide LLMs, potentially contributing to reasoning and knowledge retrieval that more closely mirror human social conceptualization.
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