FANToM: A Benchmark for Stress-testing Machine Theory of Mind in
Interactions
- URL: http://arxiv.org/abs/2310.15421v3
- Date: Tue, 31 Oct 2023 17:58:30 GMT
- Title: FANToM: A Benchmark for Stress-testing Machine Theory of Mind in
Interactions
- Authors: Hyunwoo Kim, Melanie Sclar, Xuhui Zhou, Ronan Le Bras, Gunhee Kim,
Yejin Choi, Maarten Sap
- Abstract summary: Theory of mind 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.
- Score: 94.61530480991627
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- Perceptions to Beliefs: Exploring Precursory Inferences for Theory of Mind in Large Language Models [51.91448005607405]
We evaluate key human ToM precursors by annotating characters' perceptions on ToMi and FANToM.
We present PercepToM, a novel ToM method leveraging LLMs' strong perception inference capability while supplementing their limited perception-to-belief inference.
arXiv Detail & Related papers (2024-07-08T14:58:29Z) - NegotiationToM: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surrounding [55.38254464415964]
Theory of mind evaluations currently focuses on testing models using machine-generated data or game settings prone to shortcuts and spurious correlations.
We introduce NegotiationToM, a new benchmark designed to stress-test machine ToM in real-world negotiation surrounding covered multi-dimensional mental states.
arXiv Detail & Related papers (2024-04-21T11:51:13Z) - What if...?: Thinking Counterfactual Keywords Helps to Mitigate Hallucination in Large Multi-modal Models [50.97705264224828]
We propose Counterfactual Inception, a novel method that implants counterfactual thinking into Large Multi-modal Models.
We aim for the models to engage with and generate responses that span a wider contextual scene understanding.
Comprehensive analyses across various LMMs, including both open-source and proprietary models, corroborate that counterfactual thinking significantly reduces hallucination.
arXiv Detail & Related papers (2024-03-20T11:27:20Z) - Think Twice: Perspective-Taking Improves Large Language Models'
Theory-of-Mind Capabilities [63.90227161974381]
SimToM is a novel prompting framework inspired by Simulation Theory's notion of perspective-taking.
Our approach, which requires no additional training and minimal prompt-tuning, shows substantial improvement over existing methods.
arXiv Detail & Related papers (2023-11-16T22:49:27Z) - Towards A Holistic Landscape of Situated Theory of Mind in Large
Language Models [14.491223187047378]
Large Language Models (LLMs) have generated considerable interest and debate regarding their potential emergence of Theory of Mind (ToM)
Several recent inquiries reveal a lack of robust ToM in these models and pose a pressing demand to develop new benchmarks.
We taxonomize machine ToM into 7 mental state categories and delineate existing benchmarks to identify under-explored aspects of ToM.
arXiv Detail & Related papers (2023-10-30T15:12:09Z) - HI-TOM: A Benchmark for Evaluating Higher-Order Theory of Mind Reasoning
in Large Language Models [31.831042765744204]
Theory of Mind (ToM) is the ability to reason about one's own and others' mental states.
We introduce HI-TOM, a Higher Order Theory of Mind benchmark.
Our experimental evaluation using various Large Language Models (LLMs) indicates a decline in performance on higher-order ToM tasks.
arXiv Detail & Related papers (2023-10-25T16:41:15Z) - Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in
Large Language Models [82.50173296858377]
Many anecdotal examples were used to suggest newer large language models (LLMs) like ChatGPT and GPT-4 exhibit Neural Theory-of-Mind (N-ToM)
We investigate the extent of LLMs' N-ToM through an extensive evaluation on 6 tasks and find that while LLMs exhibit certain N-ToM abilities, this behavior is far from being robust.
arXiv Detail & Related papers (2023-05-24T06:14:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.