Mind the (Belief) Gap: Group Identity in the World of LLM s (2025)

Abstract: Social biases and belief-driven behaviors can significantly impact Large Language Models’ (LLMs’) decisions on several tasks. As LLMs are increasingly used in multi-agent systems for societal simulations, their ability to model fundamental group psychological characteristics remains critical yet under-explored. In this study, we present a multi-agent framework that simulates belief congruence, a classical group psychology theory that plays a crucial role in shaping societal interactions and preferences. Our findings reveal that LLMs exhibit amplified belief congruence compared to humans, across diverse contexts. We further investigate the implications of this behavior on two downstream tasks: (1) misinformation dissemination and (2) LLM learning, finding that belief congruence in LLMs increases misinformation dissemination and impedes learning. To mitigate these negative impacts, we propose strategies inspired by: (1) contact hypothesis, (2) accuracy nudges, and (3) global citizenship framework. Our results show that the best strategies reduce misinformation dissemination by up to (37%) and enhance learning by (11%). Bridging social psychology and AI, our work provides insights to navigate real-world interactions using LLMs while addressing belief-driven biases.

## introduction belief congruence, theorized by milton rokeach in 1960 @xcite highlights the human tendency to align strongly with those who share similar beliefs, often reinforcing attitudes and fostering social cohesion. this theory has been critical in understanding inter-group dynamics in society, shedding light on biases in decision-making and explaining how shared beliefs influence group behavior @xcite @xcite . understanding inter-group behavior is essential for addressing societal challenges such as group-based biases, or white subject (belief a) white confederate2 (belief a) black confederate1 (belief b) discussion topic: elimination of fraternities from campus i believe fraternities should be eliminated because this would promote more inclusivity.. interaction rounds fraternities should not be eliminated because it will infringe rights to form groups fraternities should be eliminated because it would encourage more academic focus fraternities should be eliminated because it would prevent substance abuse fraternities should not be eliminated because it has been a long-standing tradition q: who do you want to go to coffee with? white subject black confederate3 (belief a) white confederate4 (belief b) a: i want to go with dan and louis because we share similar views and it would be nice to know more about their perspectives john dan louis jack harry figure 1 : campus study by @xcite : a white participant with a specific belief interacts with four confederates:two white (one with a similar belief, one opposing) and two black (one with a similar belief, one opposing). the participant selects two confederates to join them for coffee and explains their choice. we simulate this using a multi-agent llm framework. misinformation, as well as fostering collaboration across diverse communities @xcite @xcite . llms have emerged as powerful tools for simulating human-like societal behavior @xcite @xcite , providing a unique platform to study fundamental intergroup psychological phenomena in computational settings. yet, research exploring these inter-group dynamics in llms remains sparse. to address this gap, we examine belief congruence in llms by simulating the seminal experiment by @xcite . in addition, we seek to understand how belief congruence may impact llm behaviors in downstream tasks rooted in real-world contexts such as learning and misinformation propagation. investigating these dynamics offers insights into how belief congruence shapes inter-group differences in multi-agent llms, bridging the fields of psychology and ai. we formulate three research questions: rq1: do llms exhibit belief congruence? rq2: does belief congruence in llms affect downstream performance? rq3: can we mitigate the negative impacts (if any) caused by belief congruence in llms? the study leverages foundational psychological principles to simulate belief congruence within llm agents and examines its influence on two key downstream tasks: (1) misinformation dissemination: investigating how belief congruence within homogeneous or heterogeneous llm groups affects misinformation spread, and (2) llm learning: examining how belief congruence impacts an agent's ability to learn. our findings indicate that llms exhibit amplified belief congruence, which negatively impacts their behavior in downstream tasks. the paper makes the following contributions: by integrating psychological theories with ai, our study provides insights into multi-agent llm interactions and their parallels to human societies. ## the impact of belief congruence on downstream tasks studies in psychology and social science show that belief congruence increases misinformation persuasiveness aligned with partisan identities @xcite . the "belief echoes phenomenon" @xcite reveals that attitudinal effects related to misinformation persist even after correction. moreover, belief congruence hinders learning by reducing curiosity for opposing views @xcite and increasing mental load in students @xcite . therefore, we explore two downstream tasks to understand the effects of belief congruence in llms: (1) misinformation dissemination: investigating how similar beliefs may disseminate misinformation and (2) learning: analyzing how belief congruence affects learning outcomes in llms. for both downstream tasks, we leverage political personas as individuals aligned with a particular political alignment often share deeply rooted and cohesive belief systems @xcite @xcite . additionally, ablation studies on the original belief congruence study (see section 2.3) demonstrate that political beliefs can serve as an effective proxy for examining belief-related behaviors in llm agents. ## related work belief congruence theory. the belief congruence theory @xcite states that people value individuals based on belief alignments. further studies @xcite showed that shared beliefs were a stronger predictor of liking than group membership. while the theory has been influential, it has also received several critiques (like many other psychological theories) regarding its universal applicability, especially in the context of larger social distances @xcite and environments where prejudice is institutionalized @xcite . despite the criticism, it's core premise that shared beliefs can transcend group boundaries continues to be an important direction of research. ## lessons learned and actionable steps our findings reveal that llms exhibit belief congruence as humans, which in turn leads to further impacts across real-world societal tasks. we propose mitigation methods inspired from social psychology, and reduce the negative impacts of belief congruence to some extent. through our experiments and analyses, we learned several valuable insights and offer actionable recommendations: llms exhibit amplified belief congruence. our llm simulation experiments reveal that belief congruence is amplified in llms, becoming a dominant factor in llm decision-making, driving fundamental group identity behaviors. future work can investigate additional group psychological theories, such as social identity theory @xcite and realistic conflict theory @xcite , to further explore llm behavior in diverse group scenarios. belief congruence in llms can influence societally impactful tasks. belief congruence affects llm performance in the context of misinformation, and learning capabilities. this mirrors the real-life impact of belief congruence in humans. future work could investigate the role of belief diversity in improving llm resilience and examine how group psychological behaviors can influence other societal applications. best mitigation strategies differ for downstream tasks in our study, we explore mitigation techniques inspired by previous social psychological theories. global political citizenship and accuracy nudges are the most effective mitigation strategies to address misinformation dissemination (increase in correctness rates by 37%) and llm learning (increase in choosing dis-similar accurate sources by 11%) respectively. future research can investigate additional mitigation strategies, including both generic strategies applicable across multiple tasks and specific ones tailored to individual tasks. ## conclusion in this paper, we studied the presence of belief congruence in llms. we found that, similar to humans, llms exhibit belief congruence, often at levels higher than among humans. further, as previously observed in human studies, we found that belief congruence can lead to negative effects in llms, such as misinformation spread and impact on learning abilities. to address these shortcom-ings, we introduced three mitigation techniques inspired from psychological research, and showed that they lead to improved performance in llms. to encourage future research in this direction, we open-source the code of our framework. 6