NameRank: Measuring LLM Recognition of People and Artifacts

Jul 15, 2026

A new preprint introduces NameRank, a metric designed to quantify how well large language models (LLMs) recognize specific entities, such as people and artifacts, from their training data. The study probes 4,685 entities across 36 models and finds that LLM recognition is primarily associated with named artifacts (like papers or tools) rather than credentials or titles. Additionally, institutional prestige is found to be a stronger predictor of recognition than citation counts.

Why it matters: This work offers a systematic approach to evaluating what LLMs 'know' about individuals and artifacts, informing discussions on fairness, transparency, and the design of retrieval-augmented AI systems.

Full story at: arXiv AI/ML ↗