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    <abstract>Existing evaluations of large language models primarily rely on single-agent dilemmas or static binary-choice tasks, offering limited insight into how cooperation contexts influence LLM behavior. We introduce CoopValue, a multi-agent evaluation framework that assesses LLMs’ value preferences through cooperative scenarios. CoopValue includes 1,778 scenarios spanning all pairwise conflicts among the 10 Schwartz values and three cooperation types: reciprocal, coopetitive, and altruistic. We evaluate 24 LLMs across 8 model families and examine how their value preferences vary across different cooperative contexts, showing the importance of assessing LLM value preferences in interactive, context-sensitive settings to guide the selection and deployment of LLMs aligned with desired cooperative behavior.</abstract>
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%0 Conference Proceedings
%T CoopValue: Revealing LLM Value Preferences Through Multi-Agent Cooperation
%A Tang, Zee Hen
%A Yeh, Mi-Yen
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F tang-yeh-2026-coopvalue
%X Existing evaluations of large language models primarily rely on single-agent dilemmas or static binary-choice tasks, offering limited insight into how cooperation contexts influence LLM behavior. We introduce CoopValue, a multi-agent evaluation framework that assesses LLMs’ value preferences through cooperative scenarios. CoopValue includes 1,778 scenarios spanning all pairwise conflicts among the 10 Schwartz values and three cooperation types: reciprocal, coopetitive, and altruistic. We evaluate 24 LLMs across 8 model families and examine how their value preferences vary across different cooperative contexts, showing the importance of assessing LLM value preferences in interactive, context-sensitive settings to guide the selection and deployment of LLMs aligned with desired cooperative behavior.
%U https://aclanthology.org/2026.findings-acl.1887/
%P 37846-37885

Markdown (Informal)

[CoopValue: Revealing LLM Value Preferences Through Multi-Agent Cooperation](https://aclanthology.org/2026.findings-acl.1887/) (Tang & Yeh, Findings 2026)

• CoopValue: Revealing LLM Value Preferences Through Multi-Agent Cooperation (Tang & Yeh, Findings 2026)

ACL

• Zee Hen Tang and Mi-Yen Yeh. 2026. CoopValue: Revealing LLM Value Preferences Through Multi-Agent Cooperation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 37846–37885, San Diego, California, United States. Association for Computational Linguistics.