mlsanityData to 7 Oct 2026

LLM Theory of Mind and Alignment: Opportunities and Risks

arXiv 2405.08154 ↗First posted 13 May 2024cs.HC cs.AIcited by 15

On the map: LLM alignment / RLHF / preference optimisation → Human values and AI feedback alignment

Abstract (arXiv)

Large language models (LLMs) are transforming human-computer interaction and conceptions of artificial intelligence (AI) with their impressive capacities for conversing and reasoning in natural language. There is growing interest in whether LLMs have theory of mind (ToM); the ability to reason about the mental and emotional states of others that is core to human social intelligence. As LLMs are integrated into the fabric of our personal, professional and social lives and given greater agency to make decisions with real-world consequences, there is a critical need to understand how they can be aligned with human values. ToM seems to be a promising direction of inquiry in this regard. Following the literature on the role and impacts of human ToM, this paper identifies key areas in which LLM ToM will show up in human:LLM interactions at individual and group levels, and what opportunities and risks for alignment are raised in each. On the individual level, the paper considers how LLM ToM might manifest in goal specification, conversational adaptation, empathy and anthropomorphism. On the group level, it considers how LLM ToM might facilitate collective alignment, cooperation or competition, and moral judgement-making. The paper lays out a broad spectrum of potential implications and suggests the most pressing areas for future research.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 16 of its 63 references that are in the map. First posted on arXiv 13 May 2024.

Builds on

16 of 63 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Discovering Language Model Behaviors with Model-Written Evaluations19 Dec

Other references · 15

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  3. 2017Deep reinforcement learning from human preferencescited by 3,846
  4. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  5. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  6. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  7. 2023A Survey of Large Language Modelscited by 2,537
  8. 2021A General Language Assistant as a Laboratory for Alignmentcited by 1,023
  9. 2021Ethical and social risks of harm from Language Modelscited by 957
  10. 2020Artificial Intelligence, Values and Alignmentcited by 443
  11. 2016Cooperative Inverse Reinforcement Learningcited by 298
  12. 2023Large Language Models Fail on Trivial Alterations to Theory-of-Mind Taskscited by 288
3 more
  1. 2023Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Modelscited by 161
  2. 2018Incomplete Contracting and AI Alignmentcited by 60
  3. 2017Should Robots be Obedient?cited by 20

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 6; 2026: 5.

15 papers.

Sort:

By area:

  1. 2024LLMs achieve adult human performance on higher-order theory of mind taskscited by 59
  2. 2025Self-Resource Allocation in Multi-Agent LLM Systemscited by 13
  3. 2025Inherent and emergent liability issues in LLM-based agentic systems: a principal-agent perspectivecited by 12
  4. 2025Do Large Language Models Have a Planning Theory of Mind? Evidence from MindGames: a Multi-Step Persuasion Taskcited by 9
  5. 2024LADEV: A Language-Driven Testing and Evaluation Platform for Vision-Language-Action Models in Robotic Manipulationcited by 8
  6. 2025Assessing LLMs in Art Contexts: Critique Generation and Theory of Mind Evaluationcited by 3
  7. 2024Incentives to Build Houses, Trade Houses, or Trade House Building Skills in Simulated Worlds under Various Governing Systems or Institutions: Comparing Multi-agent Reinforcement Learning to Generative Agent-based Modelcited by 2
  8. 2024Brainstorming Brings Power to Large Language Models of Knowledge Reasoningcited by 1
  9. 2026Beyond Words: Evaluating and Bridging Epistemic Divergence in User-Agent Interaction via Theory of Mindcited by 1
  10. 2026A formal definition and meta-model for a machine theory of mindcited by 1
  11. 2025Word Synchronization Challenge: A Benchmark for Word Association Responses for Large Language Modelscited by 0
  12. 2025Theory of Mind in Action: The Instruction Inference Task in Dynamic Human-Agent Collaborationcited by 0
3 more
  1. 2026Neural Synchrony Between Socially Interacting Language Modelscited by 0
  2. 2026MindTrellis: Co-Creating Knowledge Structures with AI through Interactive Visual Explorationcited by 0
  3. 2026Theory of Mind and Persuasion Beyond Conversation: Assessing the Capacity of LLMs to Induce Belief States via Planning and Actioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Enhancing Conversational Agents with Theory of Mind: Aligning Beliefs, Desires, and Intentions for Human-Like Interaction
  2. 2025Effects of Theory of Mind and Prosocial Beliefs on Steering Human-Aligned Behaviors of LLMs in Ultimatum Games
  3. 2025Rethinking Theory of Mind Benchmarks for LLMs: Towards A User-Centered Perspective
  4. 2024Do LLMs Exhibit Human-Like Reasoning? Evaluating Theory of Mind in LLMs for Open-Ended Responses
  5. 2023Understanding Social Reasoning in Language Models with Language Models
  6. 2025Humanizing LLMs: A Survey of Psychological Measurements with Tools, Datasets, and Human-Agent Applications
  7. 2024Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina
  8. 2024Probing the Robustness of Theory of Mind in Large Language Models