PulseAugur
EN
LIVE 01:40:54

LLMs name individuals in 25.8% of responses, study finds · arXiv cs.IR

By PulseAugur Editorial · [2 sources] ·

A new study published on arXiv investigates how large language models name individual professionals in their responses, finding that models named an individual in 25.8% of queries. The research, which used 2,400 grounded API calls across four models including GPT-5.6 Sol and Gemini 3.6 Flash, revealed significant differences between models and categories, with real estate and car dealerships seeing higher naming rates. Interestingly, the type of citation, rather than citation volume, predicted naming, and prompts in English led to more individual mentions than local languages. AI

IMPACT This research highlights how LLMs attribute information, potentially impacting how AI models are evaluated for bias and transparency.

RANK_REASON The cluster contains a research paper published on arXiv detailing empirical findings about LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLMs name individuals in 25.8% of responses, study finds · arXiv cs.IR

How we ranked this

Signal score
0 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
The cluster contains a research paper published on arXiv detailing empirical findings about LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration
2 independent sources
Multiple independent publishers reporting the same story raises confidence that it's real and newsworthy.
Topics
paper, model release
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
62 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.
Coverage growth since scoring
+1 source(s) since last score
New sources have picked up this story since our last re-score. Score will update on the next scoring pass.

Full methodology in our editorial standards.

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Dmitrij \.Zatuchin (Department of Information Technologies, EUAS, Tallinn, Rankfor.AI, Tallinn) ·

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

    arXiv:2607.23893v1 Announce Type: cross Abstract: 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 …

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Dmitrij Żatuchin ·

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

    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 Ju…

RELATED ENTITIES

RELATED TOPICS