LLMScholarBench - Base Audits (datasets)
Abstract
This dataset contains the results of a systematic audit of six large language models in the context of scholar recommendation. The data include model-generated recommendations and derived evaluation outputs used in the analysis reported in "Whose Name Comes Up? Auditing LLM-Based Scholar Recommendations" (arXiv:2506.00074, version 2).
Fields of Study & Subjects
Ethics and Social Impacts of AI
Recommender Systems and Techniques
Topic Modeling
Publication details
- Publication date
- 1 January 2026
- Document type
- Preprint