# Rethinking Theory of Mind Benchmarks for LLMs: Towards A User-Centered Perspective arXiv: https://arxiv.org/abs/2504.10839 ยท HTML: https://mlsanity.com/paper/2504.10839/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. - Builds on 20 of its 49 references that are in the map. - First posted on arXiv 15 Apr 2025. ## Placed in - LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/) ## Builds on (most cited first, 20 of 20) - [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023) - [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024) - [SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents](https://mlsanity.com/paper/2310.11667/) (2023) - [Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks](https://mlsanity.com/paper/2302.08399/) (2023) - [Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs](https://mlsanity.com/paper/2210.13312/) (2022) - [FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions](https://mlsanity.com/paper/2310.15421/) (2023) - [Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models](https://mlsanity.com/paper/2305.14763/) (2023) - [ToMBench: Benchmarking Theory of Mind in Large Language Models](https://mlsanity.com/paper/2402.15052/) (2024) - [SimpleToM: Exposing the Gap between Explicit ToM Inference and Implicit ToM Application in LLMs](https://mlsanity.com/paper/2410.13648/) (2024) - [OpenToM: A Comprehensive Benchmark for Evaluating Theory-of-Mind Reasoning Capabilities of Large Language Models](https://mlsanity.com/paper/2402.06044/) (2024) - [NegotiationToM: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surrounding](https://mlsanity.com/paper/2404.13627/) (2024) - [MuMA-ToM: Multi-modal Multi-Agent Theory of Mind](https://mlsanity.com/paper/2408.12574/) (2024) - [SocialIQA: Commonsense Reasoning about Social Interactions](https://mlsanity.com/paper/1904.09728/) (2019) - [Towards A Holistic Landscape of Situated Theory of Mind in Large Language Models](https://mlsanity.com/paper/2310.19619/) (2023) - [Position: Theory of Mind Benchmarks are Broken for Large Language Models](https://mlsanity.com/paper/2412.19726/) (2024) - [On Benchmarking Human-Like Intelligence in Machines](https://mlsanity.com/paper/2502.20502/) (2025) - [Are Large Language Models More Empathetic than Humans?](https://mlsanity.com/paper/2406.05063/) (2024) - [Mutual Theory of Mind for Human-AI Communication](https://mlsanity.com/paper/2210.03842/) (2022) - [Views Are My Own, but Also Yours: Benchmarking Theory of Mind Using Common Ground](https://mlsanity.com/paper/2403.02451/) (2024) - [EgoSocialArena: Benchmarking the Social Intelligence of Large Language Models from a First-person Perspective](https://mlsanity.com/paper/2410.06195/) (2024) ## Built on by (most cited first, 14 of 14) - [From Prompts to Constructs: A Dual-Validity Framework for Large Language Model Research in Psychology](https://mlsanity.com/paper/2506.16697/) (2025) - [A validity-guided workflow for robust large language model research in psychology](https://mlsanity.com/paper/2507.04491/) (2025) - [DialToM: A Theory of Mind Benchmark for Forecasting State-Driven Dialogue Trajectories](https://mlsanity.com/paper/2604.20443/) (2026) - [MIST: Towards Multi-dimensional Implicit BiaS Evaluation of LLMs for Theory of Mind](https://mlsanity.com/paper/2506.14161/) (2025) - [Mind the Motions: Benchmarking Theory-of-Mind in Everyday Body Language](https://mlsanity.com/paper/2511.15887/) (2025) - [Situated, Dynamic, and Subjective: Envisioning the Design of Theory-of-Mind-Enabled Everyday AI with Industry Practitioners](https://mlsanity.com/paper/2602.11342/) (2026) - [GroupToM-Bench: Benchmarking Group Theory of Mind and Nonlinear Social Emergence in MLLMs](https://mlsanity.com/paper/2606.04184/) (2026) - [Growing Perspectives: Modelling Embodied Perspective Taking and Inner Narrative Development Using Large Language Models](https://mlsanity.com/paper/2509.11868/) (2025) - [When Researchers Say Mental Model/Theory of Mind of AI, What Are They Really Talking About?](https://mlsanity.com/paper/2510.02660/) (2025) - [S3IT: A Benchmark for Spatially Situated Social Intelligence Test](https://mlsanity.com/paper/2512.19992/) (2025) - [Developing the PsyCogMetrics AI Lab to Evaluate Large Language Models and Advance Cognitive Science -- A Three-Cycle Action Design Science Study](https://mlsanity.com/paper/2603.13126/) (2026) - [SCENE: Recognizing Social Norms and Sanctioning in Group Chats](https://mlsanity.com/paper/2605.07823/) (2026) - [Chatbots Output Meaningful (but Problematic) Language](https://mlsanity.com/paper/2606.02973/) (2026) - [LoSoNA: A Benchmark for Local Social Norm Adaptation in Group Conversations](https://mlsanity.com/paper/2606.14600/) (2026) ## Abstract (arXiv) The last couple of years have witnessed emerging research that appropriates Theory-of-Mind (ToM) tasks designed for humans to benchmark LLM's ToM capabilities as an indication of LLM's social intelligence. However, this approach has a number of limitations. Drawing on existing psychology and AI literature, we summarize the theoretical, methodological, and evaluation limitations by pointing out that certain issues are inherently present in the original ToM tasks used to evaluate human's ToM, which continues to persist and exacerbated when appropriated to benchmark LLM's ToM. Taking a human-computer interaction (HCI) perspective, these limitations prompt us to rethink the definition and criteria of ToM in ToM benchmarks in a more dynamic, interactional approach that accounts for user preferences, needs, and experiences with LLMs in such evaluations. We conclude by outlining potential opportunities and challenges towards this direction.