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Theory of Mind enhances LLM alignment in ultimatum games

By PulseAugur Editorial · [1 sources] ·

A new research paper explores how Theory of Mind (ToM) and prosocial beliefs influence the behavior of Large Language Models (LLMs) in ultimatum games. The study involved 2,700 simulations using LLM agents initialized with 'Greedy,' 'Fair,' or 'Selfless' beliefs and varying levels of ToM reasoning. Results indicate that ToM significantly enhances alignment with human norms, decision-making consistency, and negotiation outcomes, particularly for reasoning models. The research also found that different game roles benefit from different orders of ToM, and Llama 3.3 70B demonstrated reasoning most consistent with its actions and beliefs. AI

IMPACT This research suggests that incorporating Theory of Mind into LLMs could lead to more predictable and human-aligned AI behavior in complex social interactions.

RANK_REASON The cluster contains a research paper published on arXiv detailing experimental findings on LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Theory of Mind enhances LLM alignment in ultimatum games

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The cluster contains a research paper published on arXiv detailing experimental findings on LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Neemesh Yadav, Yihuai Lan, Shan Dong, Mai Hieu Hien, Palakorn Achananuparp, Jing Jiang, Ee-Peng Lim ·

    Effects of Theory of Mind and Prosocial Beliefs on Steering Human-Aligned Behaviors of LLMs in Ultimatum Games

    arXiv:2505.24255v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown potential in simulating human behaviors and performing theory-of-mind (ToM) reasoning, crucial for complex social interactions. We investigate ToM reasoning's role in aligning agenti…

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