Persona Prompting Boosts Expertise Depth but Reduces Clarity in LLMs

A research paper available on arXiv (2605.29420) investigated persona prompting within large language models, utilizing 1,140 open-ended queries, 38 expert roles, and six distinct domains. The study assessed four different scenarios: absence of role prompts, a generic domain-expert prompt, retrieval of roles based on embeddings, and a hybrid approach that merges embedding searches with role selection via LLM. While the overall findings indicated minimal differences, a detailed metric-level examination uncovered a consistent tradeoff: implementing role prompting enhances the depth of expertise but diminishes clarity. These effects are significantly influenced by the specific conditions applied.