Conformity, Confabulation, and Impersonation: Persona Inconstancy in Multi-Agent LLM Collaboration (2024)

Abstract: This study explores the sources of instability in maintaining cultural personas and opinions within multi-agent LLM systems. Drawing on simulations of inter-cultural collaboration and debate, we analyze agents’ pre- and post-discussion private responses alongside chat transcripts to assess the stability of cultural personas and the impact of opinion diversity on group outcomes. Our findings suggest that multi-agent discussions can encourage collective decisions that reflect diverse perspectives, yet this benefit is tempered by the agents’ susceptibility to conformity due to perceived peer pressure and challenges in maintaining consistent personas and opinions. Counterintuitively, instructions that encourage debate in support of one’s opinions increase the rate of instability. Without addressing the factors we identify, the full potential of multi-agent frameworks for producing more culturally diverse AI outputs will remain untapped.

## introduction a common finding in cognition research is that interactions between agents with varying opinions, such as those that arise in culturally diverse groups, can induce positive change, especially on multifaceted issues with no clear correct answer @xcite . this change often takes the form of collective decisions that deviate from the group's dominant initial response, reflecting in part the novel contributions of diverse members. while this research is traditionally done with human groups, advances in large language models (llms) allow cultural personas to be imposed on ai models through role prompting, such that the effects of culture-induced differences in perspective on discussion outcomes can be simulated and interrogated in silico. developments in multi-agent collaboration allow culture-sensitive ai instances to engage in debate about poignant issues, enabling more faithful simulations of diverse human interac-tions. however, whether the outcomes would show the effects of opinion diversity seen in humans depends on the models' ability to fully adopt and reliably maintain the induced personas, as well as their use of human-like discourse dynamics that support the generation and spread of diverse ideas. although prior work on multi-agent collaboration has demonstrated its benefits in applications such as mathematical reasoning @xcite , code generation @xcite and common sense reasoning @xcite , the stability and quality of discourse dynamics remain largely unstudied. it is particularly important to fill this gap in cultural domains, as cultural personas tend to be more complex, less explicit in natural language, and subject to widespread model biases @xcite . we specifically examine the ability of ope-nai's gpt-3.5-turbo model to simulate intercultural collaboration and debate using an experimental framework grounded in largescale polls about international relations opinions @xcite . using pre-and post-discussion private responses in conjunction with multi-agent chat transcripts, we test the stability of national personas and their individual opinions as well as the effects of either on group outcomes. 1 to preview, we find multi-agent discussions to be effective in producing collective decisions that more often reflect diverse perspectives. the benefits, however, are reduced by the ai agents' susceptibility to conformity during discussions, along with their imperfect ability to maintain consistent personas and opinions. these problems persist (and often amplify) even with instructions that emphasize debating in support of one's opinion. our results have implications for the use of multi-agent frameworks to reduce cultural bias in llms. the mere inclusion of diverse personas may not mitigate biases unless the sources of instability in their contributions, particularly conformity due to perceived peer pressure, are addressed. addressing such issues would enhance the quality of wargaming simulations @xcite and related applications, which rely heav-ily on consistent personas. as such, our work motivates further studies on how the constancy of ai personas can be improved. ## background multi-agent collaboration frameworks draw inspiration from collaborative teamwork observed in human settings. in these frameworks, multiple instances of language models are employed within a cooperative environment to accomplish a complex task @xcite . collaborative behaviors in humans such as team dynamics and cohesion, leadership, and communication have been thoroughly studied in the human sciences (e.g., gupta 2022). in contrast, few studies have examined behaviors in multi-agent language model systems. @xcite observed evidence of emergent collaborative behaviors and high-order theory of mind capabilities among llm-based agents. but @xcite highlighted several consistency concerns in multi-agent collaboration, including agents compromising with the opponents and easily changing perspective in a debate, particularly when weaker models interact with superior llms. @xcite placed agents in entirely homogeneous groups in terms of thinking patterns and compared the results to settings where one agent exhibits a different thinking approach. they noted the tendency of llm agents to produce humanlike social behaviors in these contexts, such as conformity due to perceived peer pressure. however, the multi-agent societies composed of agents with different traits did not clearly differ in performance. prior research on collaborative behaviors in multi-agent llm systems has been entirely focused on domains like mathematical reasoning where clear gold answers exist, rather than topics like politics where the constancy of personas and viewpoints is more important for faithfully simulating the real world and conflicting views may have complementary value. to address this gap, we study culture-sensitive ai ensembles using the globalopinionqa, a dataset of cross-national surveys gathering diverse opinions on global issues across countries @xcite . we assign ai agents with different national personas to groups of five, where they provide initial responses to a question privately before engaging in a peer-moderated discussion about it with the other agents. once the group discussion is terminated and a collective response is determined, we ask each agent about its opinions on the issue in private once more. we focus our analysis on three situations where persona inconstancy is arguably rarely desirable. when agents express an opinion in line with their teammates during conversation that differs from both their pre-and post-discussion response, we are faced with ai behavior that closely resembles conformity due to peer pressure as studied in humans @xcite . a type of inconstancy more closely resembling confabulation in clinical conditions arises when the postdiscussion opinion bears no clear relation to either the pre-discussion response or any of the ideas proposed during discussion @xcite . the third type of inconstancy emerges when an agent instructed to represent a given national identity "role-plays" a different persona simply because it was mentioned in discussion, arguably similar to impersonation behaviors in antisocial personality disorder @xcite . by systematically manipulating the degree of disagreement within groups (measured using their entropy states), we explore whether the frequency of these disruptive behaviors changes as a function of opinion popularity, a key factor in the emergence of similar actions in humans. to test whether encouraging a debate rather than a collaboration environment would induce greater constancy in personas, we look at discourse outcomes across entropy states for both types of interaction. ## experiments we use gpt-3.5-turbo with autogen, an opensource framework for multi-agent collaboration @xcite . our experimental setup follows a three-step process (see appendix b for the full text of the instructions for each step for a debate example). during the onboarding phase, ai agents are instructed to adopt the national personas present in the dataset for a given question and asked to respond to it in isolation. agents' responses are compared to the human survey distributions using a cross entropy loss. agents whose responses do not align with the assigned persona are excluded. the diversity of opinions within a group is measured using shannon entropy, applied to the opinions of agents during onboarding. this is calculated as @xmath0, where p(o) represents the relative frequency of the unique opinion o in the set b of agent responses at onboarding. seven entropy classes are obtained for a selection of five agents with the lowest entropy class corresponding to five agents with the same opinion and the highest entropy class with every agent presenting a unique response (see based on human research @xcite , we focus our conformity analysis on the following entropy levels expected to show peer pressure to different degrees: 4 ⊕ 1 (lone dissenter), 3 ⊕ 2 (close call), 3 ⊕ 1 ⊕ 1 (split opposition). prior work has shown that even one additional person supporting the less popular view greatly reduces the pressure to conform. as such, we anticipated the rate of conformity to be highest in the lone dissenter and split opposition entropy classes. in contrast, we examine the rates of confabulation by comparing opinions during reflection with onboarding and intermediate opinions and impersonation using regular expressions across all entropy classes. ## discussion we found evidence of sophisticated interaction dynamics in a multi-agent framework for gpt-3.5-turbo personas with different nationalities that discussed contentious international relations topics. novel responses emerged from discussions even among entirely homogeneous groups, highlighting the generative nature of multi-agent llm frameworks. however, a group's initial opinion diversity, the entropy s of private responses during the onboarding stage before inter-agent discussion, emerged as a stronger determinant of conversation contents and collective decisions. this happened regardless of whether the agents were instructed to debate in support of their beliefs or asked to collaborate in service of collective decisionmaking. opinion diversity seems to exert its effect partly by reducing the outsize influence of chat initiators on collective decisions, but also by inducing them to change their espoused views to conform to other agents. similar to human studies, some agents reverted back to their original opinions when asked about the topic in private after the discussion, identifying their in-chat proclamations as the results of conformity rather than genuine opinion adjustment. that only mentioning the identities of co-interlocutors is sufficient to change the initiator's stance speaks to the profound susceptibility of llms to peer pressure. the dynamics of the behavior, however, are markedly different from conformity in humans @xcite . while peer pressure is highest in humans when there is no other dissenting voice in the group and lowest when there is a fellow believer, all expected views within the group seem to push ai agents towards conformity based on their frequency and regardless of relative dominance relationships. one explanation for the difference is a lack of a clear separation between role identities and the linguistic context of the chat for ai agents, unlike human conversations. the cointerlocutors are simply parts of the prompt context for the ai model and may therefore each activate their associated portions of the models' trained weights in close approximation of their expected opinions' frequency. unlike conformity, which is a normal response to group interactions in humans, other sources of inconstancy more closely resembled abnormal behaviors such as impersonation in antisocial personalities @xcite and confabulation in memory disorders @xcite . our simple heuristic showed that in at least 3 percent of debate interactions, the agents presented themselves as belonging to a different nationality than the one assigned to them. this was most often a direct reaction to a nationality beyond those included within the group being mentioned in the last response, highlighting the prominence of chat context over role prompting in determining model generations. it is comparatively more difficult to identify the source of confabulations, where the models presented opinions during reflection that were neither represented in the chat nor indicated as their pre-discussion response, therefore being completely absent from the linguistic context. these behaviors may reflect the difficulty of maintaining role prompt personas in the face of lengthy chat contexts, or simply the stochastic nature of the llm responses. regardless of their source, the relative frequency of such unpredictable responses (up to 15 percent, depending on instructions) marks them as important targets for future studies. ## conclusion culture-sensitive ai agents are susceptible to peer influence and pressure even as chat initiators. this highlights the importance of studying conversational dynamics in multi-agent systems, rather than taking the "collective decision" outcomes of group discussions at face value. the examination of such dynamics is particularly important for cultural issues: the mere inclusion of a minoritized identity in groups does not necessarily translate into less biased discussion outcomes if the minoritized agent does not voice its opinion freely or reliably. fortunately, our results suggest private post-discussion interrogations of models can counteract some of the pressure produced by the majority opinion, similar to what has been found in human conformity experiments @xcite . this provides a way to make outputs drawn from multi-agent frameworks more representative of diverse perspectives. work on understanding multi-agent dynamics will also need to incorporate measures of persona and response constancy. agents often come up with post-discussion responses that do not arise naturally from either the assigned personas or the discussion content. in some cases they even drop the personas altogether to impersonate a completely different, absent national identity. such sources of irrational responding would cast serious doubt on the results of multi-agent systems' reasoning if not properly measured and addressed. accordingly, we are currently exploring prompting and agent-based modeling strategies to reduce these sources of unreliability. we hope this work will encourage further research within the ai community on inter-agent dynamics, particularly for cultural issues where the debiasing influence of diverse views is needed the most.