title: Tree-of-Debate: Multi-Persona Debate Trees Elicit Critical Thinking for
    Scientific Comparative Analysis
summary: 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.
paper_authors._id: 67bcb0511cc672a91331727a
paper_authors.name: Priyanka Kargupta
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-25T09:40:34.292Z
paper_authors._id: 6476ae4083d4fdaedddf405f
paper_authors.avatarUrl: /avatars/08b23ccfa1f3bede6ade5a1aef06931d.svg
paper_authors.fullname: Priyanka Kargupta
paper_authors.type: user
paper_authors.user: pkargupta
paper_authors._id: 67bcb0511cc672a91331727b
paper_authors.name: Ishika Agarwal
paper_authors._id: 67bcb0511cc672a91331727c
paper_authors.name: Tal August
paper_authors._id: 67bcb0511cc672a91331727d
paper_authors.name: Jiawei Han
paper_title: Tree-of-Debate: Multi-Persona Debate Trees Elicit Critical Thinking for
    Scientific Comparative Analysis
paper_summary: 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.
paper_ai_summary: ToD, a debate framework using LLM personas, provides structured analysis and comparison of scientific papers' novelty across domains, enhancing literature review.
paper_ai_keywords: large language models multi-agent LLM debates ToD framework debate tree independent novelty arguments scholarly articles expert researchers literature review