I nter I ntent: Investigating Social Intelligence of LLM s via Intention Understanding in an Interactive Game Context (2024)

Abstract: 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 LLM-based games.

## introduction the growing intelligence of large language models (llms) has facilitated diverse research on their capability to mimic human social intelligence @xcite @xcite @xcite . social intelligence, the ability to understand and manage one's own and others' actions and to act wisely in social relations @xcite , usually includes four key components @xcite : (1) situational awareness: the perception and comprehension of the elements in the environment @xcite . (2) self-regulation: the process of guiding one's own thoughts, behaviors, and feelings to reach goals @xcite . (3) self-awareness: the understanding of one's own character, feelings, motives, and desires @xcite . (4) theory of mind (tom): the knowledge about others @xcite . despite adaptations for interactive social environments @xcite , most existing evaluations on llms @xcite focus on straightforward and static daily scenarios without explicitly defined goals for them. to enhance the contexts with complexity and dynamics with clear goals for llms, we turn our attention to social deduction games. these games require social interactions among players, providing a more diverse test bed for llm evaluation @xcite . existing studies have successfully utilized various games to analyze llms' social behaviors, such as deception and confrontation @xcite @xcite @xcite @xcite . however, these studies often focus on ad-hoc postanalysis of game performance and overlook systematic measurement of social intelligence, limiting the comprehensive understanding of llms' capabilities in social environments. in this paper, we leverage one of the representatives in social deduction games, avalon @xcite , as the context for llm evaluation. avalon is a social deduction game that relies on conversation, making it an ideal test bed with its goal-driven objectives and complex mechanisms, facilitating interactions among llms in a grounded environment. we systematically evaluate ## intention-guided avalon framework we begin by outlining the entire game pipeline before focusing on the mechanics of intention-guided gameplay, which includes the categorization and application of intentions throughout the game. ## intention-centric evaluations this section introduces our evaluations, designed around the concept of intentions, and discusses their correlation with the four components of social intelligence outlined in sec. 1. intention is fundamental to social intelligence, essential for effective communication @xcite , strategic influence, adaptation to dynamic social interactions @xcite , and achieving desired outcomes by helping us interpret and predict behaviors @xcite . our evaluation order corresponds to the game-playing process. ## experimental settings and results we run 40 games using gpt-3.5-turbo-1106 and 5 games using gpt-4-1106, with details in appendix sec. a. sample size statistics across all experiments are presented in ## conclusion we introduce a novel framework for evaluating social intelligence within the avalon context, focusing on intentions. we examine four critical dimensions: situational awareness, self-regulation, self-awareness, and tom. our findings indicate that models demonstrate a fundamental understanding of situations and self-awareness, but struggle with self-regulation and tom, particularly when compared to human performance. through this systematic approach, our study not only contributes to a deeper understanding of llms' social intelligence but also establishes a methodological foundation for future research.