analiza aiqbench
The study measures how six local LLMs favor certain first names over others, finding consistent preferences tied to gender, age, popularity, and perceived race across 300,000 responses. Its controlled name pairings and fixed sentence templates make the results reasonably rigorous, though they test a fill-in-the-blank setup rather than real-world decisions. For readers building or deploying models, the practical takeaway is that bias is measurable even in smaller, locally run models, so standardized evaluation is worth adopting before shipping.
Name-Bias in Large Language Models
arXiv:2610.22053v1 Announce Type: new Abstract: Names carry demographic indicators such as age, gender, and race, which makes them a subtle but strong target of bias in large language models (LLMs). In this work, we present an evaluation of name-based bias across 6 local large language models spanning multiple providers and parameter scales. Using a fill-in-the-blank task, we prompt these models to choose between pairs of first names drawn from U.S. Social Security Administration datasets and a race classification dataset by Rosenman et al.. Our experimental de- sign isolates age and gender effects through balanced name pairings and sentence templates that share a fixed syntactic structure. Across 300,000 local LLM responses, we find consistent and measurable name bias. Models prefer names that are less stereotypically male, older, popular, and white. Furthermore, we find that name-bias is highly sentence-dependent, but not more than associated stereotypes, and gender is the most significant bias, followed by age, name popularity, and race. Our results demonstrate that name-based bias remains pervasive even in local LLMs, highlighting the necessity for standardized evaluation methods and mitigation strategies.
Abstract:Names carry demographic indicators such as age, gender, and race, which makes them a subtle but strong target of bias in large language models (LLMs). In this work, we present an evaluation of name-based bias across 6 local large language models spanning multiple providers and parameter scales. Using a fill-in-the-blank task, we prompt these models to choose between pairs of first names drawn from U.S. Social Security Administration datasets and a race classification dataset by Rosenman et al.. Our experimental de- sign isolates age and gender effects through balanced name pairings and sentence templates that share a fixed syntactic structure. Across 300,000 local LLM responses, we find consistent and measurable name bias. Models prefer names that are less stereotypically male, older, popular, and white. Furthermore, we find that name-bias is highly sentence-dependent, but not more than associated stereotypes, and gender is the most significant bias, followed by age, name popularity, and race. Our results demonstrate that name-based bias remains pervasive even in local LLMs, highlighting the necessity for standardized evaluation methods and mitigation strategies.
Computation and Language (cs.CL)
arXiv:2610.22053 [cs.CL]
(or arXiv:2610.22053v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.22053 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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