# Who Gets Named: Citation Type Predicts Individual Naming by Grounded Language Models, and a Roster Instrument Captures 0.5% of It

arXiv: https://arxiv.org/abs/2607.23893 · HTML: https://mlsanity.com/paper/2607.23893/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 54 references that are in the map.
- First posted on arXiv 26 Jul 2026.

## Builds on (most cited first, 18 of 18)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Neural Modeling for Named Entities and Morphology (NEMO^2)](https://mlsanity.com/paper/2007.15620/) (2020)
- [Auditing Google's AI Overviews and Featured Snippets: A Case Study on Baby Care and Pregnancy](https://mlsanity.com/paper/2511.12920/) (2025)
- [Whose Name Comes Up? Auditing LLM-Based Scholar Recommendations](https://mlsanity.com/paper/2506.00074/) (2025)
- [The Rise of AI Search: Implications for Information Markets and Human Judgement at Scale](https://mlsanity.com/paper/2602.13415/) (2026)
- [Who Owns the AI Recommendation? A Multi-Industry Empirical Map of Brand Category Ownership Across Large Language Models](https://mlsanity.com/paper/2606.23057/) (2026)
- [The Language Blind Spot: How Query Language and Brand Recognition Tier Shape AI-Constructed Brand Reputation Across Twelve European Languages](https://mlsanity.com/paper/2606.23165/) (2026)
- [Where Does the Noise Come From? A Variance-Components Decomposition of Non-Determinism in LLM Brand Answers](https://mlsanity.com/paper/2607.13304/) (2026)
- [Generating the Modal Worker: A Cross-Model Audit of Race and Gender in LLM-Generated Personas Across 41 Occupations](https://mlsanity.com/paper/2510.21011/) (2025)
- [Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems](https://mlsanity.com/paper/2606.17443/) (2026)
- [Generative Engine Optimization at Scale: Measuring Brand Visibility Across AI Search Engines](https://mlsanity.com/paper/2606.20065/) (2026)
- [How Large Language Models Source Brand Reputation Across Languages and Markets](https://mlsanity.com/paper/2606.25787/) (2026)
- [Platformization of Inequality: Gender and Race in Digital Labor Platforms](https://mlsanity.com/paper/2309.16887/) (2023)
- [A Scalable Entity-Based Framework for Auditing Bias in Large Language Models](https://mlsanity.com/paper/2601.12374/) (2026)
- [Whose Name Comes Up? II: Benchmarking and Intervention-Based Auditing of LLM-Based Scholar Recommendation](https://mlsanity.com/paper/2602.08873/) (2026)
- [What Gets Cited: Competitive GEO in AI Answer Engines](https://mlsanity.com/paper/2605.25517/) (2026)
- [Whose Name Comes Up? III: Persona Prompting Effects in LLM-Based Scholar Recommendation](https://mlsanity.com/paper/2605.28187/) (2026)
- [What Do Chinese-Language Generative Search Engines Cite and Surface? A Large-Scale Empirical Study](https://mlsanity.com/paper/2607.15771/) (2026)

## Abstract (arXiv)

Prior work on AI brand visibility measures the firm: does a model recommend a company, and does that track its reputation. This study asks the question one level down, in categories where the buyer picks a person. It issued 2,400 grounded API calls in one two-hour window on 24 July 2026: 120 buyer-intent prompts, four models (GPT-5.6 Sol, Gemini 3.6 Flash, Perplexity Sonar Pro, Grok 4.5), five iterations each, four European markets and five query languages. Every response was coded for whether it named an individual professional, by a rule cascade that never consults a roster and that drops detections resolving to a same-named American city (precision 96.9%, recall 61.7%, so every rate below is a lower bound). All inference corrects for clustering within prompt: intraclass correlation 0.258, effective n 407 against a nominal 2,400. Models named an individual in 25.8% of responses. Category dominates: real estate 35.4% and car dealerships 32.9% against insurance 9.1% (chi-square 159.3, p = 5.8e-8 after correction). Models differ four-fold, from Grok 38.0% to Gemini 9.3%. Citation type predicts naming and citation volume does not: naming responses cite the individual's own site 2.6 points more often (95% CI +1.4 to +3.9) and category portals 4.3 points more often, and cite firm-owned pages at the same rate (44.1% against 45.5%). On nine matched translation pairs, English prompts named an individual in 36.7% of responses against 15.6% for the same question in the local language (OR 3.14, clustered p = 0.074, so the direction is clear and the design cannot close it). A 939-person roster built from public LinkedIn search matched 128 of 27,293 name-shaped mentions (0.47%), 26 of the 939 people were ever named, and the roster-derived rates of 0.0% to 25.4% measure that overlap. Roster-based measurement of individual AI visibility sees a small and unrepresentative slice of what models do.