# InterIntent: Investigating Social Intelligence of LLMs via Intention Understanding in an Interactive Game Context

arXiv: https://arxiv.org/abs/2406.12203 · HTML: https://mlsanity.com/paper/2406.12203/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 60 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 30 of 32)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Efficient Intent Detection with Dual Sentence Encoders](https://mlsanity.com/paper/2003.04807/) (2020)
- [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)
- [Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf](https://mlsanity.com/paper/2309.04658/) (2023)
- [Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View](https://mlsanity.com/paper/2310.02124/) (2023)
- [Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs](https://mlsanity.com/paper/2210.13312/) (2022)
- [Understanding Social Reasoning in Language Models with Language Models](https://mlsanity.com/paper/2306.15448/) (2023)
- [Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models](https://mlsanity.com/paper/2305.14763/) (2023)
- [Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game](https://mlsanity.com/paper/2310.18940/) (2023)
- [Mindstorms in Natural Language-Based Societies of Mind](https://mlsanity.com/paper/2305.17066/) (2023)
- [Avalon's Game of Thoughts: Battle Against Deception through Recursive Contemplation](https://mlsanity.com/paper/2310.01320/) (2023)
- [Minding Language Models' (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker](https://mlsanity.com/paper/2306.00924/) (2023)
- [How FaR Are Large Language Models From Agents with Theory-of-Mind?](https://mlsanity.com/paper/2310.03051/) (2023)
- [Evaluating Theory of Mind in Question Answering](https://mlsanity.com/paper/1808.09352/) (2018)
- [Boosting Theory-of-Mind Performance in Large Language Models via Prompting](https://mlsanity.com/paper/2304.11490/) (2023)
- [LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay](https://mlsanity.com/paper/2310.14985/) (2023)
- [Is this the real life? Is this just fantasy? The Misleading Success of Simulating Social Interactions With LLMs](https://mlsanity.com/paper/2403.05020/) (2024)
- [Theory of Mind in Large Language Models: Examining Performance of 11 State-of-the-Art models vs. Children Aged 7-10 on Advanced Tests](https://mlsanity.com/paper/2310.20320/) (2023)
- [Shall We Team Up: Exploring Spontaneous Cooperation of Competing LLM Agents](https://mlsanity.com/paper/2402.12327/) (2024)
- [Hoodwinked: Deception and Cooperation in a Text-Based Game for Language Models](https://mlsanity.com/paper/2308.01404/) (2023)
- [AvalonBench: Evaluating LLMs Playing the Game of Avalon](https://mlsanity.com/paper/2310.05036/) (2023)
- [Enhance Reasoning for Large Language Models in the Game Werewolf](https://mlsanity.com/paper/2402.02330/) (2024)
- [GameEval: Evaluating LLMs on Conversational Games](https://mlsanity.com/paper/2308.10032/) (2023)
- [Cooperation on the Fly: Exploring Language Agents for Ad Hoc Teamwork in the Avalon Game](https://mlsanity.com/paper/2312.17515/) (2023)
- [COKE: A Cognitive Knowledge Graph for Machine Theory of Mind](https://mlsanity.com/paper/2305.05390/) (2023)
- [The SocialAI School: Insights from Developmental Psychology Towards Artificial Socio-Cultural Agents](https://mlsanity.com/paper/2307.07871/) (2023)
- [I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons](https://mlsanity.com/paper/2212.10060/) (2022)
- [Intent Detection with WikiHow](https://mlsanity.com/paper/2009.05781/) (2020)
- [Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia](https://mlsanity.com/paper/2207.02253/) (2022)

## Built on by (most cited first, 26 of 26)

- [On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agents](https://mlsanity.com/paper/2408.00989/) (2024)
- [SimpleToM: Exposing the Gap between Explicit ToM Inference and Implicit ToM Application in LLMs](https://mlsanity.com/paper/2410.13648/) (2024)
- [TMGBench: A Systematic Game Benchmark for Evaluating Strategic Reasoning Abilities of LLMs](https://mlsanity.com/paper/2410.10479/) (2024)
- [A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios](https://mlsanity.com/paper/2412.03920/) (2024)
- [Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers](https://mlsanity.com/paper/2502.09053/) (2025)
- [Human Cognitive Benchmarks Reveal Foundational Visual Gaps in MLLMs](https://mlsanity.com/paper/2502.16435/) (2025)
- [Triangulating LLM Progress through Benchmarks, Games, and Cognitive Tests](https://mlsanity.com/paper/2502.14359/) (2025)
- [On the Failure of Latent State Persistence in Large Language Models](https://mlsanity.com/paper/2505.10571/) (2025)
- [Bayesian Social Deduction with Graph-Informed Language Models](https://mlsanity.com/paper/2506.17788/) (2025)
- [LIFELONG SOTOPIA: Evaluating Social Intelligence of Language Agents Over Lifelong Social Interactions](https://mlsanity.com/paper/2506.12666/) (2025)
- [VIBE: Can a VLM Read the Room?](https://mlsanity.com/paper/2506.11162/) (2025)
- [Ella: Embodied Social Agents with Lifelong Memory](https://mlsanity.com/paper/2506.24019/) (2025)
- [Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation](https://mlsanity.com/paper/2503.01315/) (2025)
- [ProMediate: A Socio-cognitive framework for evaluating proactive agents in multi-party negotiation](https://mlsanity.com/paper/2510.25224/) (2025)
- [NewsInterview: a Dataset and a Playground to Evaluate LLMs' Ground Gap via Informational Interviews](https://mlsanity.com/paper/2411.13779/) (2024)
- [Social preferences with unstable interactive reasoning: Large language models in economic trust games](https://mlsanity.com/paper/2505.17053/) (2025)
- [PRISON: Unmasking the Criminal Potential of Large Language Models](https://mlsanity.com/paper/2506.16150/) (2025)
- [Social Welfare Function Leaderboard: When LLM Agents Allocate Social Welfare](https://mlsanity.com/paper/2510.01164/) (2025)
- [MafiaScope: Non-Invasive, Time-Resolved Belief Probing for LLM Agents in Social Deduction Games](https://mlsanity.com/paper/2607.10645/) (2026)
- [EUDAIMONIA: Evaluating Undesirable Dynamics in AI](https://mlsanity.com/paper/2605.30654/) (2026)
- [Theory of Mind and Persuasion Beyond Conversation: Assessing the Capacity of LLMs to Induce Belief States via Planning and Action](https://mlsanity.com/paper/2606.31916/) (2026)
- [Automated Meta Prompt Engineering for Alignment with the Theory of Mind](https://mlsanity.com/paper/2505.09024/) (2025)
- [Generating Place-Based Compromises Between Two Points of View](https://mlsanity.com/paper/2604.24536/) (2026)
- [Can Agents Deceive? Evaluating Reasoning and Deception in ParliamentBench using a Social Deduction Game](https://mlsanity.com/paper/2607.28146/) (2026)
- [Detecting AI Impostors: How Do Middle Schoolers Identify LLM Agents in a Live Collaborative Setting?](https://mlsanity.com/paper/2608.30948/) (2026)
- [PerSo: Investigating Facets of LLMs' Social Intelligence in Real-World Persuasion Scenarios](https://mlsanity.com/paper/2610.20548/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have demonstrated the potential to mimic human social intelligence. However, most studies focus on simplistic and static self-report or performance-based tests, which limits the depth and validity of the analysis. In this paper, we developed a novel framework, InterIntent, to assess LLMs' social intelligence by mapping their ability to understand and manage intentions in a game setting. We focus on four dimensions of social intelligence: situational awareness, self-regulation, self-awareness, and theory of mind. Each dimension is linked to a specific game task: intention selection, intention following, intention summarization, and intention guessing. Our findings indicate that while LLMs exhibit high proficiency in selecting intentions, achieving an accuracy of 88%, their ability to infer the intentions of others is significantly weaker, trailing human performance by 20%. Additionally, game performance correlates with intention understanding, highlighting the importance of the four components towards success in this game. These findings underline the crucial role of intention understanding in evaluating LLMs' social intelligence and highlight the potential of using social deduction games as a complex testbed to enhance LLM evaluation. InterIntent contributes a structured approach to bridging the evaluation gap in social intelligence within multiplayer games.