Defend: Automated Rebuttals for Peer Review with Minimal Author Guidance
Rebuttal generation is a critical component of the peer review process for scientific papers, enabling authors to clarify misunderstandings, correct factual inaccuracies, and guide reviewers toward a more accurate evaluation. We observe that Large Language Models (LLMs) often struggle to perform targeted refutation and maintain accurate factual grounding when used directly for rebuttal generation, highlighting the need for structured reasoning and author intervention. To address this, in the paper, we introduce DEFEND an LLM based tool designed to explicitly execute the underlying reasoning process of automated rebuttal generation, while keeping the author-in-the-loop. As opposed to writi...
| Authors | Jyotsana Khatri, Manasi Patwardhan |
|---|---|
| Primary category | cs.AI |
| Published | 28 March 2026 |
| Last updated at source | 28 March 2026 |
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