FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions

Hyunwoo Kim, Melanie Sclar, Xuhui Zhou, Ronan Le Bras, Gunhee Kim +2 more
10/24/2023
cs.CLcs.AI

No official code found. Best implementation: skywalker023/fantom (63 stars, MIT, last commit 2024-05-31).

Abstract

Theory of mind (ToM) evaluations currently focus on testing models using passive narratives that inherently lack interactivity. We introduce FANToM, a new benchmark designed to stress-test ToM within information-asymmetric conversational contexts via question answering. Our benchmark draws upon important theoretical requisites from psychology and necessary empirical considerations when evaluating large language models (LLMs). In particular, we formulate multiple types of questions that demand the same underlying reasoning to identify illusory or false sense of ToM capabilities in LLMs. We show that FANToM is challenging for state-of-the-art LLMs, which perform significantly worse than humans even with chain-of-thought reasoning or fine-tuning.

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MIT

👻 Code and benchmark for our EMNLP 2023 paper - "FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions"

639PythonOct 21, 20232 years agoMIT

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Cite this paper

@article{kim2023fantom,
  title  = {FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions},
  author = {Hyunwoo Kim and Melanie Sclar and Xuhui Zhou and Ronan Le Bras and Gunhee Kim and Yejin Choi and Maarten Sap},
  year   = {2023},
  eprint = {2310.15421},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.15421v3}
}

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