When Order Matters: First-Speaker Bias and Mitigation through Personality in Sequential Multi-Agent Debate

2026-09-30

AI Summary

This study addresses the significant first-speaker bias in sequential multi-agent debate, which causes stronger models to lose their reasoning advantage when speaking later. To tackle this issue, we first quantify the impact of such bias across varying strong-weak model configurations and construct a prompt intervention framework grounded in Big Five personality theory. Specifically, we propose low agreeableness as a behavioral modulation strategy to reshape agent influence dynamics. Experimental results demonstrate that low-agreeableness prompting effectively restores the discursive power of stronger models and improves final decision accuracy, whereas extraversion merely increases response redundancy without yielding systematic effects. This work offers a novel paradigm for optimizing the fairness and effectiveness of large language model debate mechanisms.

Abstract

Multi-agent debate (MAD) is often used to improve large language model (LLM) reasoning, but sequential debate is rarely a neutral aggregator of agents' opinions. We show that sequential MAD suffers from a pronounced first-speaker bias: agents disproportionately shape the final answer when they speak first. As a result, placing a stronger model after weaker ones can substantially offset its reasoning advantage. We then focus on the disadvantaged strong-agent-last setting and ask whether personality prompting can mitigate this imbalance. Drawing on the Big Five model, we study agreeableness and extraversion as behavioral interventions applied to either the strong or weak side. We find that their effects are trait-specific. Influence consistently shifts in the direction of lower agreeableness, and assigning low agreeableness to the stronger agent helps restore its lost influence and improves final accuracy. Extraversion, by contrast, produces less systematic changes in influence and accuracy, with its clearest effect appearing in agents' verbosity. These findings show that effective MAD design depends not only on model capability, but also on how speaking order and induced interaction behavior shape the debate process.

Problem

• Multi-agent debate
• First-speaker bias
• Large language models
• Personality prompting
• Sequential reasoning

Innovation

• Multi-agent debate
• First-speaker bias
• Personality prompting
• Big Five model
• Agreeableness