@inproceedings{tang-yeh-2026-coopvalue,
    title = "{C}oop{V}alue: Revealing {LLM} Value Preferences Through Multi-Agent Cooperation",
    author = "Tang, Zee Hen  and
      Yeh, Mi-Yen",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1887/",
    pages = "37846--37885",
    ISBN = "979-8-89176-395-1",
    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."
}