Tree-of-Debate: Multi-Persona Debate Trees Elicit Critical Thinking for
  Scientific Comparative Analysis
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.
67bcb0511cc672a91331727a
Priyanka Kargupta
claimed_verified
2025-02-25T09:40:34.292Z
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Priyanka Kargupta
user
pkargupta
67bcb0511cc672a91331727b
Ishika Agarwal
67bcb0511cc672a91331727c
Tal August
67bcb0511cc672a91331727d
Jiawei Han
Tree-of-Debate: Multi-Persona Debate Trees Elicit Critical Thinking for
  Scientific Comparative Analysis
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.
ToD, a debate framework using LLM personas, provides structured analysis and comparison of scientific papers' novelty across domains, enhancing literature review.
large language models multi-agent LLM debates ToD framework debate tree independent novelty arguments scholarly articles expert researchers literature review