Large Language Models (LLMs) have shown remarkable capabilities in general natural language processing tasks but often fall short in complex reasoning tasks. Recent studies have explored human-like problem-solving strategies, such as self-correct, to push further the boundary of single-model reasoning ability. In this work, we let a single model "step outside the box" by engaging multiple models to correct each other. We introduce a multi-agent collaboration strategy that emulates the academic peer review process. Each agent independently constructs its own solution, provides reviews on the solutions of others, and assigns confidence levels to its reviews. Upon receiving peer reviews, agents revise their initial solutions. Extensive experiments on three different types of reasoning tasks show that our collaboration approach delivers superior accuracy across all ten datasets compared to existing methods. Further study underscores the effectiveness of integrating confidence in reviews, demonstrates the superiority of feedback exchange over mere solution sharing, and highlights the role of capability and diversity in fostering successful collaboration.

翻译：大语言模型（LLMs）在通用自然语言处理任务中展现了卓越能力，但在复杂推理任务中常显不足。近期研究探索了类人问题解决策略（如自我修正），以进一步拓展单模型推理能力的边界。本研究中，我们通过让多个模型相互纠错，使单个模型“跳出思维局限”。我们提出一种模拟学术同行评审过程的多智能体协作策略：每个智能体独立构建解决方案、对其他智能体的方案进行评审，并为评审结果分配置信度等级。收到同行评审后，智能体对初始方案进行修订。在三大类推理任务上的大量实验表明，相较于现有方法，我们的协作方法在所有十个数据集上均实现了更优的准确率。进一步研究证实了在评审中融入置信度的有效性，展示了反馈交换优于单纯方案共享的优越性，并强调了能力与多样性在促进成功协作中的关键作用。