EMNLP Findings2025
MoMentS: A Comprehensive Multimodal Benchmark for Theory of Mind
Emilio Villa-Cueva, S. M. Masrur Ahmed, Rendi Chevi, Jan Christian Blaise Cruz, Kareem Elzeky, Fermin Cristobal, Alham Fikri Aji, Skyler Wang, Rada Mihalcea, Thamar Solorio
摘要
Understanding Theory of Mind is essential for building socially intelligent multimodal agents capable of perceiving and interpreting human behavior. We introduce MOMENTS (Multimodal Mental States), a comprehensive benchmark designed to assess the ToM capabilities of multimodal large language models (LLMs) through realistic, narrative-rich scenarios presented in short films. MOMENTS includes over 2,300 multiple-choice questions spanning seven distinct ToM categories. The benchmark features long video context windows and realistic social interactions that provide deeper insight into characters' mental states. We evaluate several MLLMs and find that although vision generally improves performance, models still struggle to integrate it effectively. For audio, models that process dialogues as audio do not consistently outperform transcript-based inputs. Our findings highlight the need to improve multimodal integration and point to open challenges that must be addressed to advance AI's social understanding.
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引用它的顶会 Paper2
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- MovieGraph-ToM: Evaluating Long-Range Theory of Mind in Large Language Models via Implicit Social-Causal GraphsTingjiang Wei, Qin Ni, Rong Gao, Yingying Wang 等AAAI 2026
它引用的 Paper10
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- MMToM-QA: Multimodal Theory of Mind Question AnsweringChuanyang Jin, Yutong Wu, Jing Cao, Jiannan Xiang 等ACL 2024 · 8 次引用
相关 Paper
- Theory of Mind in Large Language Models: Assessment and EnhancementRuirui Chen, Weifeng Jiang, Chengwei Qin, Cheston TanACL 2025
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