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LLMによるレビュアー-著者ディベートを用いた異種グラフ推論による自動論文査読

arXiv cs.CL / 2026/3/11

Ideas & Deep AnalysisModels & Research

要点

  • 本論文では、LLMを用いてマルチラウンドのレビュアー-著者ディベートをシミュレートし、自動論文査読を改善する新しいフレームワークReViewGraphを提案する。
  • ReViewGraphは、acceptance(採択)、rejection(却下)、clarification(明確化)、compromise(妥協)など多様な意見関係を異種グラフのタイプ付けられたエッジとしてモデル化し、これら相互作用の構造的推論を可能にする。
  • このディベートグラフをグラフニューラルネットワークで解析することで、従来の表層的特徴や直接的なLLM出力に依存した手法では捉えにくい微妙な議論の動態を捉える。
  • 3つのデータセットでの実験結果は、ReViewGraphが強力なベースラインに対し平均で15.73%の相対改善を達成し、詳細なディベート構造を取り入れたレビュー予測の有効性を検証している。
  • 本手法はレビュアー-著者交渉プロセスを明示的にモデル化することで、幻影生成や偏ったスコアリングといった自動査読における共通課題に対処し、より情報に基づいた意思決定を実現する。

Computer Science > Computation and Language

arXiv:2511.08317 (cs)
[Submitted on 11 Nov 2025 (v1), last revised 10 Mar 2026 (this version, v2)]

Title:Automatic Paper Reviewing with Heterogeneous Graph Reasoning over LLM-Simulated Reviewer-Author Debates

View a PDF of the paper titled Automatic Paper Reviewing with Heterogeneous Graph Reasoning over LLM-Simulated Reviewer-Author Debates, by Shuaimin Li and 7 other authors
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Abstract:Existing paper review methods often rely on superficial manuscript features or directly on large language models (LLMs), which are prone to hallucinations, biased scoring, and limited reasoning capabilities. Moreover, these methods often fail to capture the complex argumentative reasoning and negotiation dynamics inherent in reviewer-author interactions. To address these limitations, we propose ReViewGraph (Reviewer-Author Debates Graph Reasoner), a novel framework that performs heterogeneous graph reasoning over LLM-simulated multi-round reviewer-author debates. In our approach, reviewer-author exchanges are simulated through LLM-based multi-agent collaboration. Diverse opinion relations (e.g., acceptance, rejection, clarification, and compromise) are then explicitly extracted and encoded as typed edges within a heterogeneous interaction graph. By applying graph neural networks to reason over these structured debate graphs, ReViewGraph captures fine-grained argumentative dynamics and enables more informed review decisions. Extensive experiments on three datasets demonstrate that ReViewGraph outperforms strong baselines with an average relative improvement of 15.73%, underscoring the value of modeling detailed reviewer-author debate structures.
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2511.08317 [cs.CL]
  (or arXiv:2511.08317v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2511.08317
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arXiv-issued DOI via DataCite
Journal reference: AAAI-2026

Submission history

From: Shuaimin Li [view email]
[v1] Tue, 11 Nov 2025 14:46:07 UTC (497 KB)
[v2] Tue, 10 Mar 2026 09:44:14 UTC (452 KB)
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