--- title: "Who cites ToMBench: Benchmarking Theory — 52 citing papers" description: "52 papers of the Elamur Research library cite «ToMBench: Benchmarking Theory of Mind in Large Language Models», found in their…" canonical: "https://research.elamur.ai/free/en/papers/chen-2024-tombench-benchmarking-theory-mind-large/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/chen-2024-tombench-benchmarking-theory-mind-large/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [ToMBench: Benchmarking Theory of Mind in Large…](https://md.research.elamur.ai/free/en/papers/chen-2024-tombench-benchmarking-theory-mind-large/) / cited by # Who cites «ToMBench: Benchmarking Theory of Mind in Large Language Models» 52 papers of our library cite it (of the 149,439 whose bibliographies we read) · 13 with a page here, the most cited first · [← back to «ToMBench: Benchmarking Theory of Mind in Large Language Models»](https://md.research.elamur.ai/free/en/papers/chen-2024-tombench-benchmarking-theory-mind-large/) 1. **[Theory of Mind in Large Language Models: Assessment and Enhancement](https://md.research.elamur.ai/free/en/papers/chen-2025-theory-mind-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruirui Chen, Weifeng Jiang, Chengwei Qin, et al.*2025 · 16 citations* 2. **[Towards Dynamic Theory of Mind: Evaluating LLM Adaptation to Temporal Evolution of Human States](https://md.research.elamur.ai/free/en/papers/xiao-2025-dynamic-theory-mind-evaluating-llm/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yang Xiao, Jiashuo Wang, Qiancheng Xu, et al.*2025 · 8 citations* 3. **[NOVER: Incentive Training for Language Models via Verifier-Free Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/liu-2025-nover-incentive-training-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wei Liu, Siya Qi, Xinyu Wang, et al.*2025 · 7 citations* 4. **[A Review of Incorporating Psychological Theories in LLMs](https://md.research.elamur.ai/free/en/papers/liu-2026-review-incorporating-psychological-theories-llms/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Zizhou Liu, Ziwei Gong, Lin Ai, et al.*2026 · 1 citation* 5. **[MAESTRO: Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization](https://md.research.elamur.ai/free/en/papers/zhao-2026-maestro-meta-learning-adaptive-estimation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yang Zhao, Hepeng Wang, Xiao Ding, et al.*2026 · 1 citation* 6. **[Can Large Language Models Infer Human Actions and Motives? Evaluation in Social Prediction and Inspection Games](https://md.research.elamur.ai/free/en/papers/shrestha-2026-can-large-language-models-infer/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kaleen Shrestha, Abhinav Gupta, Harish Dukkipati, et al.*2026* 7. **[Can Small LLMs Learn a Robust Theory of Mind via RLVR? Investigating Generalization through the False-Belief Task](https://md.research.elamur.ai/free/en/papers/sarangi-2026-can-small-llms-learn-robust/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sneheel Sarangi, Hanan Salam*2026* 8. **[Danger Depends on the Mind: A Theory-of-Mind Grounded Dataset and Model for Context-Dependent Dangerous Speech](https://md.research.elamur.ai/free/en/papers/shi-2026-danger-depends-mind-theory-mind/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuanchen Shi, Longyin Zhang, Guodong Zhou, et al.*2026* 9. **[GroupToM-Bench: Benchmarking Group Theory of Mind and Nonlinear Social Emergence in MLLMs](https://md.research.elamur.ai/free/en/papers/tang-2026-grouptom-bench-benchmarking-group-theory/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weidong Tang, Jierui Li, Yueling Hou, et al.*2026* 10. **[HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs](https://md.research.elamur.ai/free/en/papers/peng-2026-hss-synth-humanities-social-sciences/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ru Peng, Tianyu Zhao, Xijun Gu, et al.*2026* 11. **[HumanLLM: Benchmarking and Improving LLM Anthropomorphism via Human Cognitive Patterns](https://md.research.elamur.ai/free/en/papers/wang-2026-humanllm-benchmarking-improving-llm-anthropomorphism/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xintao Wang, Jian Yang, Weiyuan Li, et al.*2026* 12. **[Let's Put Ourselves in Sally's Shoes: Shoes-of-Others Prefilling Improves Theory of Mind in Large Language Models](https://md.research.elamur.ai/free/en/papers/shinoda-2026-let-s-put-ourselves-sally/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kazutoshi Shinoda, Nobukatsu Hojo, Kyosuke Nishida, et al.*2026* 13. **[ToMELP: A Theory-of-Mind Benchmark for Route-Controlled Persuasion under the Elaboration Likelihood Model](https://md.research.elamur.ai/free/en/papers/wang-2026-tomelp-theory-mind-benchmark-route/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ruirui Wang, Haoran Zhang, Tian Lan, et al.*2026* 39 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/chen-2024-tombench-benchmarking-theory-mind-large/) — ToMBench: Benchmarking Theory of Mind in Large Language Models