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LLM Theory of Mind and Alignment: Opportunities and Risks

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arxiv 2405.08154 v1 pith:A5FQT75D submitted 2024-05-13 cs.HC cs.AI

classification cs.HCcs.AI
keywords humanalignmentllmsareasconsidersgroupindividualintelligence
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Do Large Language Models Have a Planning Theory of Mind? Evidence from MindGames: a Multi-Step Persuasion Task

    cs.CL 2025-07 conditional novelty 6.0 of 10

    In a new persuasion game, humans outperformed the LLM o1-preview when an opponent's preferences had to be inferred, while o1-preview outperformed humans when those preferences were disclosed.

  2. MAGPIE: A dataset for Multi-AGent contextual PrIvacy Evaluation

    cs.AI 2025-06 conditional novelty 6.0 of 10

    MAGPIE is a 158-scenario benchmark showing large language model agents misclassify and leak contextually private information in multi-agent collaboration, even under explicit privacy instructions.

  3. Where You Go is Who You Are: Behavioral Theory-Guided LLMs for Inverse Reinforcement Learning

    cs.AI 2025-05 conditional novelty 6.0 of 10

    SILIC uses LLM-guided inverse reinforcement learning and Theory of Planned Behavior chain reasoning to infer age, gender, income, and employment from travel trajectories, reportedly beating SVM, XGBoost, CatBoost, and...

  4. Towards Machine Theory of Mind with Large Language Model-Augmented Inverse Planning

    cs.AI 2025-07 conditional novelty 4.0 of 10

    An LLM-augmented Bayesian inverse planning model, LAIP, generates hypotheses and action likelihoods, then uses Bayes' rule to infer agent preferences, outperforming LLM-only baselines.

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