CoopValue: Revealing LLM Value Preferences Through Multi-Agent Cooperation

ACL ARR 2026 January Submission7433 Authors

Keywords: Value preferences, multi-agent cooperation

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.

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: NLP datasets, evaluation methodologies

Contribution Types: Data resources

Languages Studied: English

Submission Number: 7433