ICLR 2025

Published as a conference paper at ICLR 2025
BREAKING MENTAL SET TO IMPROVE REASONING
THROUGH DIVERSE MULTI-AGENT DEBATE
Yexiang Liu1,2, Jie Cao1∗, Zekun Li3, Ran He1,2, Tieniu Tan1,2,4
1MAIS & NLPR, Institute of Automation, Chinese Academy of Sciences
2School of Artificial Intelligence, University of Chinese Academy of Sciences
3University of California, Santa Barbara
4Nanjing University
liuyexiang2023@ia.ac.cn, jie.cao@cripac.ia.ac.cn
ABSTRACT
Large Language Models (LLMs) have seen significant progress but continue to
struggle with persistent reasoning mistakes. Previous methods of self-reflection
have been proven limited due to the models’ inherent fixed thinking patterns.
While Multi-Agent Debate (MAD) attempts to mitigate this by incorporating mul-
tiple agents, it often employs the same reasoning methods, even though assigning
different personas to models. This leads to a “fixed mental set”, where models rely
on homogeneous thought processes without exploring alternative perspectives. In
this paper, we introduce Diverse Multi-Agent Debate (DMAD), a method that en-
courages agents to think with distinct reasoning approaches. By leveraging diverse
problem-solving strategies, each agent can gain insights from different perspec-
tives, refining its responses through discussion and collectively arriving at the op-
timal solution. DMAD effectively breaks the limitations of fixed mental sets. We
evaluate DMAD against various prompting techniques, including self-reflection
and traditional MAD, across multiple benchmarks using both LLMs and Multi-
modal LLMs. Our experiments show that DMAD consistently outperforms other
methods, delivering better results than MAD in fewer rounds. Code is available at
https://github.com/MraDonkey/DMAD.
1
INTRODUCTION
In the past few years, Large Language Models (LLMs) have experienced unprecedented develop-
ment (Brown et al., 2020; Touvron et al., 2023a;b; Chowdhery et al., 2023; Anil et al., 2023; Achiam
et al., 2023; Open AI, 2024a),...