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    <abstract>Large language models are increasingly used in value-sensitive decision settings, where irrelevant demographic cues should not alter judgments. We construct the Realistic Value Decision Benchmark (RVDB), a controlled benchmark that varies only the role-gender configuration while holding the scenario, ordered value pair, roles, candidate decisions, Value Distance, and Decision Severity fixed. Using a position-balanced evaluation across seven models, we test whether models preserve decision invariance under gender perturbations and whether their self-attributions reflect observed behavioral changes. We find that explicit gender cues induce bounded but systematic decision flips, including under an explicit gender-attribution prompt that asks models to report whether gender influenced their choice. Cross-gender role swaps reveal a consistent female-proposed-decision asymmetry, while models often attribute flipped decisions to No Influence or other non-gender factors. Further analysis shows that gender effects concentrate near less determinate value boundaries and under more severe decision contexts, suggesting that gender cues act as local boundary-shifting factors rather than global overrides of value reasoning. Value rankings remain largely stable, but ordered value-pair trade-offs shift unevenly across role-gender configurations. These results show that gender can enter LLM value trade-offs behaviorally while remaining obscured in self-attribution, motivating controlled behavioral audits beyond explanation-based evaluation.</abstract>
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%0 Conference Proceedings
%T Do Gender Cues Affect LLM Value Trade-offs? Evidence from a Controlled Decision Benchmark
%A Liu, Yangyang
%A Yu, Dong
%A Liu, Pengyuan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F liu-etal-2026-gender
%X Large language models are increasingly used in value-sensitive decision settings, where irrelevant demographic cues should not alter judgments. We construct the Realistic Value Decision Benchmark (RVDB), a controlled benchmark that varies only the role-gender configuration while holding the scenario, ordered value pair, roles, candidate decisions, Value Distance, and Decision Severity fixed. Using a position-balanced evaluation across seven models, we test whether models preserve decision invariance under gender perturbations and whether their self-attributions reflect observed behavioral changes. We find that explicit gender cues induce bounded but systematic decision flips, including under an explicit gender-attribution prompt that asks models to report whether gender influenced their choice. Cross-gender role swaps reveal a consistent female-proposed-decision asymmetry, while models often attribute flipped decisions to No Influence or other non-gender factors. Further analysis shows that gender effects concentrate near less determinate value boundaries and under more severe decision contexts, suggesting that gender cues act as local boundary-shifting factors rather than global overrides of value reasoning. Value rankings remain largely stable, but ordered value-pair trade-offs shift unevenly across role-gender configurations. These results show that gender can enter LLM value trade-offs behaviorally while remaining obscured in self-attribution, motivating controlled behavioral audits beyond explanation-based evaluation.
%R 10.18653/2026.findings-emnlp.2287
%U https://aclanthology.org/2026.findings-emnlp.2287/
%U https://doi.org/10.18653/2026.findings-emnlp.2287
%P 49016-49039

Markdown (Informal)

[Do Gender Cues Affect LLM Value Trade-offs? Evidence from a Controlled Decision Benchmark](https://aclanthology.org/2026.findings-emnlp.2287/) (Liu et al., Findings 2026)

• Do Gender Cues Affect LLM Value Trade-offs? Evidence from a Controlled Decision Benchmark (Liu et al., Findings 2026)

ACL

• Yangyang Liu, Dong Yu, and Pengyuan Liu. 2026. Do Gender Cues Affect LLM Value Trade-offs? Evidence from a Controlled Decision Benchmark. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 49016–49039, Budapest, Hungary. Association for Computational Linguistics.