辩论之树：多角色辩论框架激发批判性思维，助力科学对比分析

摘要

随着现代技术推动的研究呈指数级增长以及获取渠道的改善，科学发现在各领域内部及跨领域间愈发呈现碎片化态势。这使得评估相关研究的重要性、创新性、渐进性发现以及等价观点变得尤为困难，尤其是那些来自不同研究群体的工作。近期，大型语言模型（LLMs）在定量与定性推理能力上展现出强大实力，而多智能体LLM辩论通过探索多元视角与推理路径，在处理复杂推理任务方面显示出潜力。受此启发，我们提出了“辩论树”（Tree-of-Debate, ToD）框架，该框架将科学论文转化为LLM角色，让它们就各自的创新点展开辩论。ToD强调结构化、批判性的推理过程，而非仅关注结果，它动态构建辩论树，实现对学术文章中独立创新论点的细致分析。通过跨多个科学领域的文献实验，并由专家研究者评估，我们证明ToD能够生成信息丰富的论点，有效对比论文，并为研究者的文献综述提供有力支持。

English

With the exponential growth of research facilitated by modern technology and improved accessibility, scientific discoveries have become increasingly fragmented within and across fields. This makes it challenging to assess the significance, novelty, incremental findings, and equivalent ideas between related works, particularly those from different research communities. Large language models (LLMs) have recently demonstrated strong quantitative and qualitative reasoning abilities, and multi-agent LLM debates have shown promise in handling complex reasoning tasks by exploring diverse perspectives and reasoning paths. Inspired by this, we introduce Tree-of-Debate (ToD), a framework which converts scientific papers into LLM personas that debate their respective novelties. To emphasize structured, critical reasoning rather than focusing solely on outcomes, ToD dynamically constructs a debate tree, enabling fine-grained analysis of independent novelty arguments within scholarly articles. Through experiments on scientific literature across various domains, evaluated by expert researchers, we demonstrate that ToD generates informative arguments, effectively contrasts papers, and supports researchers in their literature review.

辩论之树：多角色辩论框架激发批判性思维，助力科学对比分析

Tree-of-Debate: Multi-Persona Debate Trees Elicit Critical Thinking for Scientific Comparative Analysis

摘要

Summary

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