Papers with Theory-of-Mind
ECHo: A Visio-Linguistic Dataset for Event Causality Inference via Human-Centric Reasoning (2023.findings-emnlp)
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| Challenge: | ECHo is a diagnostic dataset of event causality inference grounded in visio-linguistic social scenarios. |
| Approach: | They propose a diagnostic dataset of event causality inference grounded in visio-linguistic social scenarios. |
| Outcome: | The proposed framework examines the reasoning capability of current AI systems on three human-centric tasks. |
Are LLMs Effective Negotiators? Systematic Evaluation of the Multifaceted Capabilities of LLMs in Negotiation Dialogues (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are increasingly being utilized as AI negotiation agents . however, prior research on LLMs lacks a systematic evaluation of their diverse capabilities in negotiation. |
| Approach: | They propose to analyze the multifaceted capabilities of Large Language Models (LLMs) across diverse dialogue scenarios throughout the stages of a typical negotiation interaction. |
| Outcome: | The proposed model outperforms GPT-4 in many negotiation tasks while identifying specific challenges, such as making subjective assessments and generating contextually appropriate, strategically advantageous responses. |
DEL-ToM: Inference-Time Scaling for Theory-of-Mind Reasoning via Dynamic Epistemic Logic (2025.emnlp-main)
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| Challenge: | Theory-of-Mind (ToM) tasks pose a unique challenge for large language models (LLMs), which often lack the capability for dynamic logical reasoning. |
| Approach: | They propose a framework that decomposes ToM tasks into a sequence of belief updates grounded in Dynamic Epistemic Logic (DEL) they use data generated automatically via a DEL simulator to train a verifier, which is called the Process Belief Model (PBM). |
| Outcome: | The proposed framework improves verifiable ToM reasoning through inference-time scaling rather than architectural changes. |
EnigmaToM: Improve LLMs’ Theory-of-Mind Reasoning Capabilities with Neural Knowledge Base of Entity States (2025.findings-acl)
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| Challenge: | Existing ToM reasoning methods rely excessively on off-the-shelf LLMs, reducing their efficiency and limiting their applicability to high-order ToM. |
| Approach: | They propose a neuro-symbolic framework that integrates a Neural Knowledge Base of Entity States and knowledge injection to enhance ToM reasoning. |
| Outcome: | The proposed framework improves ToM reasoning on ToMi, HiToM, and FANToM benchmarks. |
The Essence of Contextual Understanding in Theory of Mind: A Study on Question Answering with Story Characters (2025.acl-long)
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Chulun Zhou, Qiujing Wang, Mo Yu, Xiaoqian Yue, Rui Lu, Jiangnan Li, Yifan Zhou, Shunchi Zhang, Jie Zhou, Wai Lam
| Challenge: | Theory-of-Mind (ToM) is a psychological capability that allows humans to understand and interpret the mental states of others. |
| Approach: | They propose a CharToM-QA benchmark to assess the importance of comprehensive contextual understanding about personal backgrounds in ToM. |
| Outcome: | The proposed model outperforms existing models on 1,035 ToM questions based on classic novels and shows that educated participants perform better when they have read the novels than non-educated participants. |
Beyond Context to Cognitive Appraisal: Emotion Reasoning as a Theory of Mind Benchmark for Large Language Models (2025.findings-acl)
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| Challenge: | Recent studies have shown that large language models (LLMs) reason about others' emotional states using contextual information, within a Theory-of-Mind framework. |
| Approach: | They propose to use large language models to reason about others’ emotional states using contextual information within a Theory-of-Mind framework. |
| Outcome: | The proposed models can reason about situations and appraisals, but are poor at associating situational outcomes and appraisal with specific emotions. |