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Gender bias across LLMs is common and highly heterogenous

Paper recorded by Signals 4 on 2026-09-29 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-29 on arXiv · recorded by Signals 4 on 2026-09-30

Category: cs.AI · 人工智能 · first seen 2026-09-30

Abstract

Understanding gender biases in large language models (LLMs) is increasingly important as these systems become embedded in decision-support tools with real consequences. Prior research has focused only on a small set of models, leaving open the extent to which gender biases are common and heterogeneous across LLMs. We address this gap across ten models released between April 2025 and June 2026, spa

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#19 most recent of 460 cs.AI papers we have recorded · ↑ newer: UserProxyBench: Evaluating LLM User Simulators for Agent Benchmarks an · ↓ older: doPlan: A Variable-Horizon Dataset for Multi-Stage Language-Conditione
Cite this page: Gender bias across LLMs is common and highly heterogenous: the #19 most recent of 460 cs.AI papers we have recorded (as of 2026-09-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/gender-bias-across-llms-is-common-and-highly-heterogenous.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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