Exploring the Impact of Occupational Personas on Domain-Specific QA

TL;DR

This paper investigates how different types of personas influence domain-specific question-answering performance in large language models, revealing that relevance alone does not ensure improvement and may sometimes hinder accuracy.

Contribution

It introduces and empirically evaluates two types of personas—Profession-Based and Occupational Personality-Based—and analyzes their effects on scientific domain QA tasks.

Findings

PBPs slightly improve accuracy

OPBPs often degrade performance

Persona relevance does not guarantee better knowledge use

Abstract

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.