mlsanityData to 14 Oct 2026

Exploring Jiu-Jitsu Argumentation for Writing Peer Review Rebuttals

arXiv 2311.03998 ↗First posted 7 Nov 2023cs.CLcited by 18

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Abstract (arXiv)

In many domains of argumentation, people's arguments are driven by so-called attitude roots, i.e., underlying beliefs and world views, and their corresponding attitude themes. Given the strength of these latent drivers of arguments, recent work in psychology suggests that instead of directly countering surface-level reasoning (e.g., falsifying given premises), one should follow an argumentation style inspired by the Jiu-Jitsu 'soft' combat system (Hornsey and Fielding, 2017): first, identify an arguer's attitude roots and themes, and then choose a prototypical rebuttal that is aligned with those drivers instead of invalidating those. In this work, we are the first to explore Jiu-Jitsu argumentation for peer review by proposing the novel task of attitude and theme-guided rebuttal generation. To this end, we enrich an existing dataset for discourse structure in peer reviews with attitude roots, attitude themes, and canonical rebuttals. To facilitate this process, we recast established annotation concepts from the domain of peer reviews (e.g., aspects a review sentence is relating to) and train domain-specific models. We then propose strong rebuttal generation strategies, which we benchmark on our novel dataset for the task of end-to-end attitude and theme-guided rebuttal generation and two subtasks.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 24 of its 46 references that are in the map. First posted on arXiv 7 Nov 2023.

Builds on

24 of 46 references.

References · 24

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
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  11. 2018A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applicationscited by 136
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12 more
  1. 2019A Large-scale Dataset for Argument Quality Ranking: Construction and Analysiscited by 67
  2. 2022NLPeer: A Unified Resource for the Computational Study of Peer Reviewcited by 60
  3. 2019Automatic Argument Quality Assessment -- New Datasets and Methodscited by 47
  4. 2019Argument Mining for Understanding Peer Reviewscited by 41
  5. 2021DISAPERE: A Dataset for Discourse Structure in Peer Review Discussionscited by 36
  6. 2019Does My Rebuttal Matter? Insights from a Major NLP Conferencecited by 31
  7. 2020Aspect-based Sentiment Analysis of Scientific Reviewscited by 24
  8. 2021Argument Undermining: Counter-Argument Generation by Attacking Weak Premisescited by 22
  9. 2021DS-TOD: Efficient Domain Specialization for Task Oriented Dialogcited by 21
  10. 2022Fair and Argumentative Language Modeling for Computational Argumentationcited by 18
  11. 2020Argument Mining Driven Analysis of Peer-Reviewscited by 16
  12. 2019Clustering of Deep Contextualized Representations for Summarization of Biomedical Textscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 9; 2026: 7.

18 papers.

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  1. 2025Large language models for automated scholarly paper review: A surveycited by 61
  2. 2025Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluationcited by 42
  3. 2024What Can Natural Language Processing Do for Peer Review?cited by 25
  4. 2025Re2: A Consistency-ensured Dataset for Full-stage Peer Review and Multi-turn Rebuttal Discussionscited by 23
  5. 2025PeerQA: A Scientific Question Answering Dataset from Peer Reviewscited by 22
  6. 2025How Far Are AI Scientists from Changing the World?cited by 15
  7. 2025LazyReview A Dataset for Uncovering Lazy Thinking in NLP Peer Reviewscited by 13
  8. 2025Insights from the ICLR Peer Review and Rebuttal Processcited by 11
  9. 2026Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistancecited by 9
  10. 2026RbtAct: Rebuttal as Supervision for Actionable Review Feedback Generationcited by 6
  11. 2026Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Futurecited by 6
  12. 2025OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Modelscited by 5
6 more
  1. 2026DRPG (Decompose, Retrieve, Plan, Generate): An Agentic Framework for Academic Rebuttalcited by 5
  2. 2026Author-in-the-Loop Response Generation and Evaluation: Integrating Author Expertise and Intent in Responses to Peer Reviewcited by 4
  3. 2026Defend: Automated Rebuttals for Peer Review with Minimal Author Guidancecited by 4
  4. 2025Decision-Making with Deliberation: Meta-reviewing as a Document-grounded Dialoguecited by 3
  5. 2024SEAGraph: Unveiling the Whole Story of Paper Review Commentscited by 2
  6. 2026Reviewing the Reviewer: LLM-Assisted Reviewer Feedback Generation for Guideline Compliancecited by 0