---
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---
# Review-5K: A Dataset for Peer Review Analysis

HomePage: https://wengsyx.github.io/Researcher/ 

### Review-5K Dataset

The Review-5K dataset is a collection of peer reviews and associated metadata from the ICLR 2024 conference. It is designed to facilitate research on the *analysis* of the peer review process itself, *not* to be used for automating or replacing human review in real-world settings.

The dataset is constructed by:

1.  **Data Collection:** Gathering paper information (title, abstract, PDF) and corresponding review comments from ICLR 2024 via OpenReview.
2.  **Data Retrieval:** Attempting to retrieve permitted LaTeX files from ArXiv.  If unavailable, using MagicDoc to convert PDFs to markdown.
3.  **Data Structuring:** Organizing each data point to reflect the peer review process, including:
    *   Summary of the work
    *   Identified strengths and weaknesses
    *   Questions for clarification
    *   Numerical scores (soundness, presentation, contribution, overall rating)
    *   Meta-review information (if available)
4.  **Data Filtering:** Filtering out incomplete or blank data points.
5. **Data Split**: The dataset is splited into training/testing sets.

The dataset contains 4,991 papers and over 16,000 reviewer comments.

### Example

```json
{
  "id": "LEYUkvdUhq",               // Unique identifier (likely from OpenReview)
  "title": "ZipIt! Merging Models from Different Tasks without Training",
  "decision": "## Paper Decision\n\n\nAccept (poster)",
  "review_contents": [             // List of reviews
    {
      "content": "### Reviewer\\n\\n### Summary\\n\\nThe paper..."
    },
   {
      "content": "### Reviewer\\n\\n### Summary\\n\\nThe paper..."
    },
  ],
  "messages": [                   // List of messages
    {
       "content": "You are an expert academic reviewer tasked with providing a thorough and balanced evaluation of research papers...",
        "role": "system"
    },
    {
       "content": "Title: ZipIt! Merging Models from Different Tasks without Training\n\nABSTRACT\n...",
        "role": "user"
    },
    {
       "content": "## Reviewer\n\n### Summary\n\nThis paper proposes a novel method for merging two different models...",
        "role": "assistant"
    },
  ],
  "rates": [6, 5, 6, 6],            // Example numerical ratings (details may vary)
}
```

*   **`id`**:  A unique identifier for the paper and its associated reviews (the OpenReview ID).
*   **`title`**: The title of the reviewed paper.
*    **`decision`**: Paper decision.
*   **`review_contents`**:  A list of individual reviews.  Each review is a dictionary with (at minimum) a `content` field containing the text of the review. It may *also* include structured fields like "Soundness," "Presentation," etc., if extracted.
* **`messages`**: The training dataset.
*   **`rates`**:  A list of numerical scores provided by the reviewers. The exact meaning and order of these scores should be documented (e.g., [Soundness, Presentation, Contribution, Overall]).

## Using Review-5K

You can easily download and use the Review-5k dataset with Hugging Face's [datasets](https://huggingface.co/datasets) library.

```python
from datasets import load_dataset

dataset = load_dataset("WestlakeNLP/Review-5K")
print(dataset)
```

Alternatively, stream the dataset:

```python
from datasets import load_dataset

dataset = load_dataset("WestlakeNLP/Review-5K", streaming=True)
print(dataset)
print(next(iter(dataset['train'])))
```

## Model Specifications
|            Model Name             |                 Pre-training Language Model                  |                           HF Link                            |  MS Link   |
| :-------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :--------: |
|    CycleReviewer-ML-Llama3.1-8B    | [Llama3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) | [🤗 link](https://huggingface.co/WestlakeNLP/CycleReviewer-ML-Llama3.1-8B) | [🤖 TODO]() |
|   CycleReviewer-ML-Llama3.1-70B    | [Llama3.1-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct) | [🤗 link](https://huggingface.co/WestlakeNLP/CycleReviewer-ML-Llama3.1-70B) | [🤖 TODO]() |
|     CycleReviewer-ML-123B      | [Mistral-Large-2](https://huggingface.co/mistralai/Mistral-Large-Instruct-2407) | [🤗 link](https://huggingface.co/WestlakeNLP/CycleReviewer-ML-123B) | [🤖 TODO]() |

The CycleReviewer model is trained in Review-5K.

## CITE

```
@inproceedings{
weng2025cycleresearcher,
title={CycleResearcher: Improving Automated Research via Automated Review},
author={Yixuan Weng and Minjun Zhu and Guangsheng Bao and Hongbo Zhang and Jindong Wang and Yue Zhang and Linyi Yang},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=bjcsVLoHYs}
}
```

### Open Source License

The code in this repository is open-sourced under the Apache-2.0 license.  The datasets are open-sourced under the Review-5K Dataset License.