Textual Time Travel: A Temporally Informed Approach to Theory of Mind (2021.findings-emnlp)
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| Challenge: | Existing models that attribute mental states to oneself and others perform poorly on false belief tasks where beliefs differ from reality. |
| Approach: | They propose a temporally informed approach for improving the theory of mind capability of memory-augmented neural models by integrating priors about entities’ minds and tracking their mental states over time through an extended passage. |
| Outcome: | The proposed model improves performance on false belief tasks where beliefs differ from reality, especially when the dataset contains distracting sentences. |
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| Challenge: | Existing benchmarks assess basic Theory of Mind abilities but neglect temporal evolution of mental states in real-world social contexts. |
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| Challenge: | Theory of Mind (ToM) is a cornerstone of human social intelligence . Large Language Models (LLMs) are increasingly integrated into daily life . |
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Chulun Zhou, Qiujing Wang, Mo Yu, Xiaoqian Yue, Rui Lu, Jiangnan Li, Yifan Zhou, Shunchi Zhang, Jie Zhou, Wai Lam
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