arXiv cs.CL· Kalyan Bhetwal, Ceren Mert, Ian Whitehouse·· 2 天前AI 评分31
大语言模型中的姓名偏见研究
Name-Bias in Large Language Models
AI 导读
一项针对6个本地大语言模型的评估发现，模型在填空任务中系统性偏好刻板印象中男性化程度更低、更年长、更常见及白人姓名。研究基于30万条模型回复，发现性别偏见最显著，其次为年龄、姓名流行度和种族，且姓名偏见高度依赖句子模板。
正文 · 原文
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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.22053 [cs.CL] |
| (or arXiv:2610.22053v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.22053 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Kalyan Bhetwal [view email]
[v1]
Thu, 15 Oct 2026 15:19:55 UTC (99 KB)
来源：arXiv cs.CL · arxiv.org