Decompose-ToM: Enhancing Theory of Mind Reasoning in Large Language Models through Simulation and Task Decomposition (2025.coling-main)
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| Challenge: | Theory of Mind (ToM) is the ability to attribute and infer the mental states of others. |
| Approach: | They propose an LLM-based inference algorithm that improves model performance on complex ToM tasks by simulating user perspectives. |
| Outcome: | The proposed algorithm improves model performance on complex ToM tasks while requiring minimal prompt tuning across tasks and no additional model training. |
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| Challenge: | Theory of Mind (ToM) is a critical component of intelligence but its assessment remains the subject of heated debates. |
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Chunkit Chan, Yauwai Yim, Hongchuan Zeng, Zhiying Zou, Xinyuan Cheng, Zhifan Sun, Zheye Deng, Kawai Chung, Yuzhuo Ao, Fan Yixiang, Cheng Jiayang, Ercong Nie, Ginny Wong, Helmut Schmid, Hinrich Schuetze, Simon See, Yangqiu Song
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| Challenge: | Existing approaches to simulating Theory of Mind (ToM) using LLMs have been a longstanding problem in natural language processing (NLP). |
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Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models (2024.eacl-long)
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Natalie Shapira, Mosh Levy, Seyed Hossein Alavi, Xuhui Zhou, Yejin Choi, Yoav Goldberg, Maarten Sap, Vered Shwartz
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