Whose Name Comes Up? III: Persona Prompting Effects in LLM-Based Scholar Recommendation
Large language models (LLMs) are increasingly used as scholar recommenders, shaping who is seen as an expert in academia. Existing audits remain English-centric, single discipline, and persona-agnostic, leaving the source of output variability poorly understood. To this end, we propose a benchmark that disentangles the effects of model choice and prompt design on recommendations. We audit 43 LLMs by varying persona prompts (language, location, role-and-task) and context (field, seniority, k). Recommended scholars are compared against Semantic Scholar over six scientific disciplines to measure technical quality (factuality, coverage) and social representativeness (diversity, parity). Basic...
| Authors | Annabella Sánchez-Guzmán, Lukas Eberhard, Denis Helic et al. |
|---|---|
| Primary category | cs.IR |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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