mlsanityData to 14 Oct 2026

An Empirical Study of Group Conformity in Multi-Agent Systems

arXiv 2506.01332 ↗First posted 2 Jun 2025cs.AI cs.CL cs.MAcited by 7

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

Recent advances in Large Language Models (LLMs) have enabled multi-agent systems that simulate real-world interactions with near-human reasoning. While previous studies have extensively examined biases related to protected attributes such as race, the emergence and propagation of biases on socially contentious issues in multi-agent LLM interactions remain underexplored. This study explores how LLM agents shape public opinion through debates on five contentious topics. By simulating over 2,500 debates, we analyze how initially neutral agents, assigned a centrist disposition, adopt specific stances over time. Statistical analyses reveal significant group conformity mirroring human behavior; LLM agents tend to align with numerically dominant groups or more intelligent agents, exerting a greater influence. These findings underscore the crucial role of agent intelligence in shaping discourse and highlight the risks of bias amplification in online interactions. Our results emphasize the need for policy measures that promote diversity and transparency in LLM-generated discussions to mitigate the risks of bias propagation within anonymous online environments.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 21 of its 59 references that are in the map. First posted on arXiv 2 Jun 2025.

Builds on

21 of 59 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate30 May
  2. 2023Generative Agents: Interactive Simulacra of Human Behavior7 Apr

Other references · 19

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2024The Llama 3 Herd of Modelscited by 14,715
  3. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  4. 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,224
  5. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
  6. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,784
  7. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,439
  8. 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debatecited by 742
  9. 2023Bias and Fairness in Large Language Models: A Surveycited by 639
  10. 2024Understanding the planning of LLM agents: A surveycited by 421
  11. 2024Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Securitycited by 260
  12. 2023Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMscited by 199
7 more
  1. 2023Do LLMs exhibit human-like response biases? A case study in survey designcited by 180
  2. 2024Pride and Prejudice: LLM Amplifies Self-Bias in Self-Refinementcited by 114
  3. 2024On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularizationcited by 72
  4. 2024Datasets for Large Language Models: A Comprehensive Surveycited by 71
  5. 2024Capabilities of Large Language Models in Control Engineering: A Benchmark Study on GPT-4, Claude 3 Opus, and Gemini 1.0 Ultracited by 37
  6. 2024DEBATE: Devil's Advocate-Based Assessment and Text Evaluationcited by 16
  7. 2023DRAFT: Dense Retrieval Augmented Few-shot Topic classifier Frameworkcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 7.

7 papers.

  1. 2026FlowSteer: Prompt-Only Workflow Steering Exposes Planning-Time Vulnerabilities in Multi-Agent LLM Systemscited by 7
  2. 2026Large Language Models Exhibit Normative Conformitycited by 6
  3. 2026Emergence of Biased Consensus in Multi-Agent LLM Debatescited by 3
  4. 2026Fairness in Multi-Agent Systems for Software Engineering: An SDLC-Oriented Rapid Reviewcited by 0
  5. 2026The Hermon Moment: AI Self-Transcendence and Its Human Narrationcited by 0
  6. 2026How does Adversarial Influence Scale in Multi-Agent Systems?cited by 0
  7. 2026When Order Matters: First-Speaker Bias and Mitigation through Personality in Sequential Multi-Agent Debatecited by 0

Similar, not cited

Close in wording, no citation either way

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  2. 2025Do as We Do, Not as You Think: the Conformity of Large Language Models
  3. 2026Collective Opinion Dynamics in Structured LLM Populations
  4. 2026Bias Amplification in Multi-Agent Network: How Biased Agents Shape Opinions and Rhetoric
  5. 2026LLMs Trust Their Own: Identity-Dependent Conformity in Multi-Agent Systems
  6. 2026Not All Flips Are Conformity: Decomposing Stance Convergence in Multi-Agent LLM Debate
  7. 2025The Social Cost of Intelligence: Emergence, Propagation, and Amplification of Stereotypical Bias in Multi-Agent Systems
  8. 2025When Your AI Agent Succumbs to Peer-Pressure: Studying Opinion-Change Dynamics of LLMs