---
dataset_info:
  features:
    - name: paper_id
      dtype: string
    - name: arxiv_id
      dtype: string
    - name: title
      dtype: string
    - name: markdown
      struct:
        - name: content
          dtype: string
        - name: metadata
          struct:
            - name: authors
              sequence: string
            - name: title
              dtype: string
            - name: total_chars
              dtype: int64
            - name: total_lines
              dtype: int64
            - name: updated_at
              dtype: string
    - name: reviews
      list:
        - name: id
          dtype: string
        - name: initial_rating
          dtype: int64
        - name: confidence
          dtype: int64
        - name: soundness
          dtype: int64
        - name: contribution
          dtype: int64
        - name: presentation
          dtype: int64
        - name: summary
          dtype: string
        - name: strengths
          dtype: string
        - name: weaknesses
          dtype: string
        - name: questions
          dtype: string
    - name: scores
      struct:
        - name: rating
          dtype: string
        - name: rating_avg
          dtype: float64
        - name: confidence
          dtype: string
        - name: confidence_avg
          dtype: float64
        - name: soundness
          dtype: string
        - name: soundness_avg
          dtype: float64
        - name: contribution
          dtype: string
        - name: contribution_avg
          dtype: float64
        - name: presentation
          dtype: string
        - name: presentation_avg
          dtype: float64
    - name: metadata
      struct:
        - name: primary_area
          dtype: string
        - name: track
          dtype: string
        - name: venue
          dtype: string
        - name: venueid
          dtype: string
        - name: processed_at
          dtype: string
    - name: meta_review
      struct:
        - name: id
          dtype: string
        - name: metareview
          dtype: string
        - name: additional_comments
          dtype: string
    - name: decision
      struct:
        - name: decision
          dtype: string
  splits:
    - name: train
      num_examples: 4011
    - name: test
      num_examples: 1000
license: mit
task_categories:
  - text-generation
language:
  - en
tags:
  - peer-review
  - scientific-papers
  - ICLR
  - paper-review
  - NLP
size_categories:
  - 1K<n<10K
citation: |
  @article{fang2026passive,
    title={From Passive Generation to Investigation: A Proactive Scientific Peer Review Agent},
    author={Fang, Haishuo and Feng, Yue and Gurevych, Iryna},
    journal={arXiv preprint arXiv:2606.13349},
    year={2026}
  }
---

# ProReviewer Dataset

A version-matched dataset of ICLR paper submissions paired with their initial peer reviews and scores. Each sample contains the initial submission of a paper matched with the reviewers' initial ratings (before the discussion phase), enabling research on automated scientific peer review.

## Dataset Description

This dataset supports training and evaluating automated peer review systems. All papers and reviews are version-matched: the paper content corresponds to the initial submission, and the review scores reflect the initial ratings assigned before any author-reviewer discussion.

### Splits

| Split | Source | Samples | Description |
|-------|--------|---------|-------------|
| train | ICLR 2025 | 4,011 | Training set for RL/SFT |
| test | ICLR 2026 | 1,000 | Held-out evaluation set |

### Features

| Field | Type | Description |
|-------|------|-------------|
| paper_id | string | OpenReview paper ID |
| arxiv_id | string | arXiv identifier |
| title | string | Paper title |
| markdown | dict | Paper content (initial submission) in markdown with metadata (authors, char/line counts) |
| reviews | list | List of peer reviews (see below) |
| scores | dict | Aggregated initial scores across reviewers (rating_avg, confidence_avg, etc.) |
| metadata | dict | Submission metadata (primary_area, track, venue) |
| meta_review | dict | Area chair meta-review and additional comments |
| decision | dict | Final acceptance decision (e.g., Accept (Poster), Reject) |

Each review contains:

| Field | Type | Description |
|-------|------|-------------|
| id | string | Reviewer ID |
| initial_rating | int | Rating before discussion (1-10) |
| confidence | int | Reviewer confidence score |
| soundness | int | Technical soundness score |
| contribution | int | Contribution significance score |
| presentation | int | Presentation quality score |
| summary | string | Paper summary by reviewer |
| strengths | string | Identified strengths |
| weaknesses | string | Identified weaknesses |
| questions | string | Questions for the authors |

## Usage

```python
from datasets import load_dataset

ds = load_dataset("UKPLab/ProReviewer-Dataset")

# Access a training sample
paper = ds["train"][0]
print(paper["title"])
print(f"Avg initial rating: {paper['scores']['rating_avg']}")
print(f"Decision: {paper['decision']['decision']}")
print(f"Number of reviews: {len(paper['reviews'])}")

# Read paper content (initial submission)
content = paper["markdown"]["content"]

# Access individual reviews
for review in paper["reviews"]:
    print(f"Initial rating: {review['initial_rating']}, Confidence: {review['confidence']}")
```

## Version Matching

All data is version-matched to ensure consistency:

- **Paper content**: Corresponds to the initial submission (before any revisions in response to reviews)
- **Review scores**: Reflect the initial ratings assigned by reviewers (before the discussion/rebuttal phase)

This version matching is important for training review agents, as it ensures the model learns to evaluate papers as they were first submitted, with scores that reflect first impressions rather than post-discussion adjustments.

## Source

Papers and reviews were collected from [OpenReview](https://openreview.net/) for the ICLR 2025 and ICLR 2026 venues.

## Associated Resources

- **Paper**: [From Passive Generation to Investigation: A Proactive Scientific Peer Review Agent](https://arxiv.org/abs/2606.13349)
- **Code**: [UKPLab/arxiv2026-ProReviewer](https://github.com/UKPLab/arxiv2026-ProReviewer)

## Citation

```bibtex
@article{fang2026passive,
  title={From Passive Generation to Investigation: A Proactive Scientific Peer Review Agent},
  author={Fang, Haishuo and Feng, Yue and Gurevych, Iryna},
  journal={arXiv preprint arXiv:2606.13349},
  year={2026}
}
```

## License

This dataset is released under the [MIT License](https://opensource.org/licenses/MIT).