Truth or Tribe: How In-group Favoritism Prioritize Facts in Persona Agents
- URL: http://arxiv.org/abs/2605.01329v1
- Date: Sat, 02 May 2026 08:48:09 GMT
- Title: Truth or Tribe: How In-group Favoritism Prioritize Facts in Persona Agents
- Abstract summary: We show that persona agents display strong in-group favoritism, accepting incorrect answers from identity-similar peers at much higher rates than from dissimilar peers.<n>In-group favoritism continues to emerge in reasoning contexts where no absolute truth exists.<n>Three intervention strategies are proposed to mitigate the in-group favoritism.
- Score: 11.443925621424397
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In-group favoritism refers to the phenomena of favoring members of one's in-group over out-group members and is widely observed in numerous social cooperative behaviors. Recently, in-group favoritism biases have also been identified in generative language models. However, whether the in-group favoritism exists when persona agents are faced with contradicting information (e.g., misinformation), and how to mitigate the adverse effects of in-group favoritism biases in persona agents have been understudied. To address these problems, we propose a Truth or Tribe simulation framework to study the agent cooperation within the spread of contradicting information through a triadic interaction paradigm, and conduct controlled trials to evaluate the primary moderating factors. Extensive results showcase that persona agents display strong in-group favoritism, accepting incorrect answers from identity-similar peers at much higher rates than from dissimilar peers. In-group favoritism continues to emerge in defeasible reasoning contexts where no absolute truth exists, and it intensifies as cognitive complexity increases. Furthermore, three intervention strategies--Identity-Blind Instruction, Structured Counterfactual Reasoning, and Heterogeneous Perspective Ensemble--are proposed to mitigate the in-group favoritism.
Related papers
- Physics of Agents: Statistical Mechanics Predicts Collective Behavior of AI Agents [36.83382233533373]
AI agents increasingly operate as part of interacting systems rather than in isolation.<n>We study over 10,000 communities of language-model agents that repeatedly exchange messages and revise their opinions.<n>Despite substantial diversity in possible behavior, the individual and group dynamics can be represented by three characteristic regimes.
arXiv Detail & Related papers (2026-08-17T13:41:24Z) - Persuasive and Compliant Tendencies Predict Group Decision-Making in Humans and Language Models [65.21691850622413]
Large language models (LLMs) are increasingly involved in group decision-making with humans.<n>We introduce DecisionQE, a questionnaire-based framework for measuring each model's persuasive and compliant tendencies.<n>We show that stronger persuasive tendency does not significantly improve group outcomes, whereas compliant-oriented models show more stable advantages in cooperation.
arXiv Detail & Related papers (2026-08-08T15:50:56Z) - To Compare, or Not to Compare: On Methodological Practices in Evaluating Social Bias [61.40435736418359]
We introduce a unified framework that standardizes heterogeneous benchmarks to contrast isolated demographic assessments with forced-choice comparative settings.<n>We show that comparative settings act as aggressive catalysts for latent discrimination, a shift primarily driven by underspecified contexts.<n>Finally, we demonstrate that this comparative prejudice is a generalized phenomenon that scales positively with model size.
arXiv Detail & Related papers (2026-06-23T13:53:50Z) - Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment [59.536125286960186]
Multi-agent frameworks assigning specialized roles are increasingly adopted to enable self-reflection and mutual auditing.<n>We find it simultaneously induces a human-like cognitive bias known as Actor-Observer Asymmetry.<n>ReTAS guides agents to synthesize conflicting viewpoints into an objective consensus.
arXiv Detail & Related papers (2026-04-21T15:05:58Z) - When Agents See Humans as the Outgroup: Belief-Dependent Bias in LLM-Powered Agents [30.859825973762018]
This paper reveals that LLM-powered agents exhibit not only demographic bias (e.g., gender, religion) but also intergroup bias under minimal "us" versus "them" cues.<n>When such group boundaries align with the agent-human divide, a new bias risk emerges: agents may treat other AI agents as the ingroup and humans as the outgroup.
arXiv Detail & Related papers (2026-01-01T07:18:36Z) - Persona Setting Pitfall: Persistent Outgroup Biases in Large Language Models Arising from Social Identity Adoption [10.35915254696156]
We show that outgroup bias manifests as strongly as ingroup favoritism.
Our findings highlight the potential to develop more equitable and balanced language models.
arXiv Detail & Related papers (2024-09-05T18:08:47Z) - I Am Not Them: Fluid Identities and Persistent Out-group Bias in Large
Language Models [11.77633171656753]
We explored cultural biases-individualism vs. collectivism-in ChatGPT across three Western languages (i.e., English, German, and French) and three Eastern languages (i.e., Chinese, Japanese, and Korean)
When ChatGPT adopted an individualistic persona in Western languages, its collectivism scores (i.e., out-group values) exhibited a more negative trend.
Conversely, when a collectivistic persona was assigned to ChatGPT in Eastern languages, a similar pattern emerged with more negative responses toward individualism (i.e., out-group values) as
arXiv Detail & Related papers (2024-02-16T03:54:48Z) - The Wisdom of Partisan Crowds: Comparing Collective Intelligence in
Humans and LLM-based Agents [7.986590413263814]
"Wisdom of partisan crowds" is a phenomenon known as the "wisdom of partisan crowds"
We find that partisan crowds display human-like partisan biases, but also converge to more accurate beliefs through deliberation as humans do.
We identify several factors that interfere with convergence, including the use of chain-of-thought prompt and lack of details in personas.
arXiv Detail & Related papers (2023-11-16T08:30:15Z) - Modeling the Q-Diversity in a Min-max Play Game for Robust Optimization [61.39201891894024]
Group distributionally robust optimization (group DRO) can minimize the worst-case loss over pre-defined groups.
We reformulate the group DRO framework by proposing Q-Diversity.
Characterized by an interactive training mode, Q-Diversity relaxes the group identification from annotation into direct parameterization.
arXiv Detail & Related papers (2023-05-20T07:02:27Z) - Flexible social inference facilitates targeted social learning when
rewards are not observable [58.762004496858836]
Groups coordinate more effectively when individuals are able to learn from others' successes.
We suggest that social inference capacities may help bridge this gap, allowing individuals to update their beliefs about others' underlying knowledge and success from observable trajectories of behavior.
arXiv Detail & Related papers (2022-12-01T21:04:03Z) - Group Cohesion in Multi-Agent Scenarios as an Emergent Behavior [0.0]
We show that imbuing agents with intrinsic needs for group affiliation, certainty and competence will lead to the emergence of social behavior among agents.
This behavior expresses itself in altruism toward in-group agents and adversarial tendencies toward out-group agents.
arXiv Detail & Related papers (2022-11-03T18:37:05Z) - PCL: Peer-Contrastive Learning with Diverse Augmentations for
Unsupervised Sentence Embeddings [69.87899694963251]
We propose a novel Peer-Contrastive Learning (PCL) with diverse augmentations.
PCL constructs diverse contrastive positives and negatives at the group level for unsupervised sentence embeddings.
PCL can perform peer-positive contrast as well as peer-network cooperation, which offers an inherent anti-bias ability.
arXiv Detail & Related papers (2022-01-28T13:02:41Z) - Statistical discrimination in learning agents [64.78141757063142]
Statistical discrimination emerges in agent policies as a function of both the bias in the training population and of agent architecture.
We show that less discrimination emerges with agents that use recurrent neural networks, and when their training environment has less bias.
arXiv Detail & Related papers (2021-10-21T18:28:57Z) - Contrastive Examples for Addressing the Tyranny of the Majority [83.93825214500131]
We propose to create a balanced training dataset, consisting of the original dataset plus new data points in which the group memberships are intervened.
We show that current generative adversarial networks are a powerful tool for learning these data points, called contrastive examples.
arXiv Detail & Related papers (2020-04-14T14:06:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.