Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia
Chandler Smith, Marwa Abdulhai, Manfred Díaz, Marko Tesic, Rakshit Trivedi, Sasha Vezhnevets, Lewis Hammond, Jesse Clifton, Minsuk Chang, Edgar Duenez-Guzman, John Agapiou, Jayd Matyas, Danny Karmon, Beining Zhang, Jim Dilkes, Akash Kundu, Hieu Minh Nguyen, Emanuel Tewolde, Jebish Purbey, Ram Mohan Rao Kadiyala, Siddhant Gupta, Aliaksei Korshuk, Buyantuev Alexander, Ilya Makarov, Gang Zhao, Rolando Fernandez, Zhihan Wang, Caroline Wang, Jiaxun Cui, Lingyun Xiao, Di Shi, Yoonchang Sung, Muhammad Arrasy Rahman, Peter Stone, Yipeng Kang, Hyeonggeun Yun, Ananya Ananya, Taehun Cha, Zhiqiang Wu, Elizaveta Tennant, Olivia Macmillan-Scott, Marta Segura, Diana Riazi, Fuyang Cui, Sriram Ganapathi, Toryn Klassen, Nico Schiavone, Mogtaba Alim, Sheila McIlraith, Manuel Rios, Oswaldo Peña, Carlos Rojas, Manuela Viviana Chacon-Chamorro, Ruben Manrique, Luis Felipe Giraldo, Nicanor Quijano, Yiding Wang, Yuxuan Chen, Fangwei Zhong, Mengmeng Wang, Wenming Tu, Zhaowei Zhang, Ziang Chen, Zixia Jia, Xue Feng, Zilong Zheng, Chichen Lin, Weijian Fan, Chenao Liu, Sneheel Sarangi, Ziyan Wang, shuqing shi, Yali Du, Avinaash Anand Kulandaivel, Yang Liu, Wu Ruiyang, Chetan Talele, 陆孙嘉, Gema Parreno, Shamika Dhuri, Bain McHale, Tim Baarslag, Dylan Hadfield-Menell, Natasha Jaques, José Hernández-Orallo, Joel Leibo
Large language model (LLM) agents have demonstrated impressive capabilities for social interaction and are increasingly being deployed in situations where they might engage with both human and artificial agents. These interactions represent a critical frontier for LLM-based agents, yet existing evaluation methods fail to measure how well these capabilities generalize to novel social situations. In this paper, we introduce a method for evaluating the ability of LLM-based agents to cooperate in zero-shot, mixed-motive environments using Concordia, a natural language multi-agent simulation environment. This work introduces an approach to measuring human-appropriate cooperative intelligence, emphasizing an agent's ability to identify and exploit opportunities for mutual gain across diverse partners and contexts. We present empirical results from the NeurIPS 2024 Concordia Contest, where agents were evaluated on their ability to achieve mutual gains across a suite of diverse scenarios ranging from negotiation to collective action problems. Our findings reveal significant gaps between current agent capabilities and the robust generalization required for reliable cooperation, particularly in scenarios demanding persuasion and norm enforcement.