Exploring the Impact of Occupational Personas on Domain-Specific QA
This work addresses the problem of optimizing persona use in specialized QA for AI researchers, but it is incremental as it builds on existing persona studies with mixed results.
This study investigated whether personas improve domain-specific question-answering performance, finding that profession-based personas slightly increased accuracy while occupational personality-based personas often degraded it.
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.