@inproceedings{chan-etal-2024-negotiationtom,
    title = "{N}egotiation{T}o{M}: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surrounding",
    author = "Chan, Chunkit  and
      Jiayang, Cheng  and
      Yim, Yauwai  and
      Deng, Zheye  and
      Fan, Wei  and
      Li, Haoran  and
      Liu, Xin  and
      Zhang, Hongming  and
      Wang, Weiqi  and
      Song, Yangqiu",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.findings-emnlp.244/",
    doi = "10.18653/v1/2024.findings-emnlp.244",
    pages = "4211--4241",
    abstract = "Large Language Models (LLMs) have sparked substantial interest and debate concerning their potential emergence of Theory of Mind (ToM) ability. Theory of mind evaluations currently focuses on testing models using machine-generated data or game settings prone to shortcuts and spurious correlations, which lacks evaluation of machine ToM ability in real-world human interaction scenarios. This poses a pressing demand to develop new real-world scenario benchmarks. We introduce NegotiationToM, a new benchmark designed to stress-test machine ToM in real-world negotiation surrounding covered multi-dimensional mental states (i.e., desires, beliefs, and intentions). Our benchmark builds upon the Belief-Desire-Intention (BDI) agent modeling theory and conducts the necessary empirical experiments to evaluate large language models. Our findings demonstrate that NegotiationToM is challenging for state-of-the-art LLMs, as they consistently perform significantly worse than humans, even when employing the chain-of-thought (CoT) method."
}