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From Language to Country to Valence: A Multilingual Audit of Stereotype Bias Across LLMs

Paper recorded by Signals 4 on 2026-11-02 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-11-02 on arXiv · recorded by Signals 4 on 2026-11-03

Category: cs.CL · 自然语言处理 · first seen 2026-11-03

Abstract

Large language models (LLMs) are increasingly considered for education, recruitment, workplace decision support, and other consequential applications, making pre-deployment social-bias detection essential. Yet major benchmarks are English-centred or translated, focus mainly on negative stereotypes, and may miss linguistic and cultural variation. We investigate gender, racial, and religious bias us

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#24 most recent of 628 cs.CL papers we have recorded · ↑ newer: They Know Where It Is, But Not What It Says: A Bilingual Hallucination · ↓ older: SMILE: A Structured Multi-Agent Clinical Reasoning for Liver Ultrasoun
Cite this page: From Language to Country to Valence: A Multilingual Audit of Stereotype Bias Across LLMs: the #24 most recent of 628 cs.CL papers we have recorded (as of 2026-11-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/from-language-to-country-to-valence-a-multilingual-audit-of-stereotype-bias-acro.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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