README.md

metadata

pretty_name: RMR-75K
language:
  - en
license: mit
task_categories:
  - text-generation
tags:
  - datasets
  - peer-review
  - scientific-text

🚀 RMR-75K

📌 RMR-75K (Review-Map-Rebuttal) is a large-scale segment-level mapping dataset that links review weakness/question key points to the specific rebuttal span that addresses them, and annotates each pair with

• a review perspective label (7 categories) and
• a rebuttal impact category (5 levels) reflecting the author’s reaction and degree of uptake.

📊 Dataset size

• Total mappings: 75,542
• Total papers: 4,825
• Distinct reviews: 16,583
• Avg. mappings per paper: 15.66
• Avg. mappings per review: 4.56
• Conference source: ICLR 2024

📁 Data format

Each line is a JSON object (JSONL). One object corresponds to one mapped review key point and its aligned rebuttal response span, with labels.

🔑 Fields

• paper_title The paper title.
• paper_id The OpenReview submission id.
• conference The source venue and year, for example ICLR-2024.
• review_id Identifier of the review the segment comes from.
• weakness_content The atomic weakness or question segment extracted from the review.
• perspective One of 7 review perspective labels.
• rebuttal_content The rebuttal span that addresses weakness_content.
• rebuttal_label One of 5 rebuttal impact categories.

🧪 Example

{
  "paper_title": "Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages",
  "paper_id": "zzqn5G9fjn",
  "conference": "ICLR-2024",
  "review_id": "UQfBBoocAY",
  "weakness_content": "Although the paper is generally well-structured, the title mentions `low-resource` languages ... I would suggest ... include more tasks ... MasakhaNEWS ...",
  "perspective": "Experiments",
  "rebuttal_content": "Thank you for recommending these excellent datasets for our evaluation. ... we have initiated experiments with MasakhaNEWS ... Table 2 ...",
  "rebuttal_label": "CRP"
}

🧭 Label taxonomy

Review perspective labels (7)

Each review segment has exactly one perspective label:

Perspective	Definition (brief)
Experiments	Experimental setup/design: missing/insufficient experiments, weak baselines, missing ablations, unclear datasets/splits, hyperparameters/seeds, compute/training details.
Evaluation	Metrics/analysis/interpretation: missing or inappropriate metrics, lack of statistical testing or error bars, insufficient analysis, inconsistencies between claims and results.
Reproducibility	Reproducibility details: missing code/data/links, missing hyperparameters, unclear preprocessing, seeds, hardware, insufficient instructions to replicate results.
Novelty	Originality/positioning vs prior work: incremental contribution, overlap, unclear differentiation, missing related work.
Theory	Theoretical correctness/justification: flawed assumptions, gaps in proofs, incorrect derivations, mismatch between theorems and algorithms.
Writing	Clarity/readability: grammar/style, ambiguous phrasing, undefined terms/symbols, confusing explanations.
Presentation	Figures/tables/organization: unclear plots/legends, formatting issues, misplaced/redundant content, overall structure hard to follow.

Rebuttal impact categories (5)

Each aligned rebuttal span has exactly one impact label:

Label	Meaning (brief)
CRP	Concrete Revision Performed: authors point to specific changes or verifiable artifacts already added.
SRP	Specific Revision Plan: concrete future edits are committed with where/what to revise, but not yet implemented.
VCR	Vague Commitment to Revise: promises to improve without actionable details.
DWC	Defend Without Change: argues the paper already addresses the point; no edits proposed.
DRF	Deflect/Reframe: shifts responsibility or reframes the issue; no change offered.

📉 Label distribution (RMR-75K)

Counts and percentages for Perspective × Impact:

Perspective (total)	CRP	SRP	VCR	DWC	DRF
Evaluation (11,257)	4,766 (42.3%)	903 (8.0%)	171 (1.5%)	5,249 (46.6%)	168 (1.5%)
Experiments (25,160)	12,059 (47.9%)	2,272 (9.0%)	401 (1.6%)	9,833 (39.1%)	595 (2.4%)
Novelty (8,585)	2,828 (32.9%)	872 (10.2%)	185 (2.2%)	4,578 (53.3%)	122 (1.4%)
Presentation (4,776)	2,894 (60.6%)	803 (16.8%)	256 (5.4%)	784 (16.4%)	39 (0.8%)
Reproducibility (4,402)	2,009 (45.6%)	465 (10.6%)	120 (2.7%)	1,747 (39.7%)	61 (1.4%)
Theory (12,822)	4,253 (33.2%)	1,110 (8.7%)	282 (2.2%)	6,859 (53.5%)	318 (2.5%)
Writing (8,540)	4,693 (55.0%)	1,149 (13.5%)	631 (7.4%)	1,997 (23.4%)	70 (0.8%)
Overall	33,502 (44.3%)	7,574 (10.0%)	2,046 (2.7%)	31,047 (41.1%)	1,373 (1.8%)

🎯 Intended use

RMR-75K is designed for:

• training and evaluating perspective-conditioned review feedback generation
• leveraging rebuttal outcomes as weak supervision for multiple dimensions such as actionability
• studying the relationship between review and rebuttal responses

📝 Citation

If you find this dataset useful in your research, please cite:

@misc{wu2026rbtactrebuttalsupervisionactionable,
  title={RbtAct: Rebuttal as Supervision for Actionable Review Feedback Generation},
  author={Sihong Wu and Yiling Ma and Yilun Zhao and Tiansheng Hu and Owen Jiang and Manasi Patwardhan and Arman Cohan},
  year={2026},
  eprint={2603.09723},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2603.09723},
}