---
pretty_name: SurveyReview
language:
- en
tags:
- survey-evaluation
- peer-review
- benchmark
- text
task_categories:
- text-classification
- text-generation
---

# SurveyReview

SurveyReview is a reviewer-aligned benchmark for evaluating survey papers. It turns real peer-review reports into multidimensional scores and rationales so that model judgments can be compared with human reviewer judgments.

The benchmark covers four dimensions: **Readability**, **Criticalness**, **Comprehensiveness**, and **Structure**.

> **Latest release: [`v1.1`](https://huggingface.co/datasets/brighterrluo/SurveyReview/tree/main/v1.1)**

## What's New in v1.1

- Cleaned full-text content for 1,646 survey articles, stored in two JSON shards.
- The original train/test labels and benchmark dimensions are preserved.
- Evaluation prompts now request structured `<reason>...</reason> <score>X</score>` responses.
- An API-based evaluation pipeline reports MSE and MAE for scores and can optionally use a judge model to compute Rationale Quality Score (RQS).
- Lightweight LLaMA-Factory recipes are included for Qwen3-32B LoRA supervised fine-tuning on each of the four dimensions.

See the [v1.1 release README](https://huggingface.co/datasets/brighterrluo/SurveyReview/blob/main/v1.1/README.md) for setup, evaluation, and benchmark results.

## Versions

| Version | Status | Description |
| --- | --- | --- |
| [`v1.1/`](https://huggingface.co/datasets/brighterrluo/SurveyReview/tree/main/v1.1) | Latest | Cleaned article texts, XML-style rationale/score prompts, evaluation code, and training recipes. |
| [`v1.0-paper/`](https://huggingface.co/datasets/brighterrluo/SurveyReview/tree/main/v1.0-paper) | Archived | Paper-aligned data plus Marker Markdown and Grobid XML resources for reproduction. |

## v1.1 Layout

| Path | Contents |
| --- | --- |
| `v1.1/data/v1.1-paper/raw/` | Review-level train and test samples used for the reported data statistics. |
| `v1.1/data/v1.1-paper/train/` | Training labels grouped by survey. |
| `v1.1/data/v1.1-paper/test/` | Test labels grouped by survey. |
| `v1.1/data/v1.1-paper/articles/` | Cleaned article full texts, split into two JSON shards. |
| `v1.1/data/v1.1-paper/prompt/` | Dimension definitions, evaluation prompts, and the RQS judge prompt. |
| `v1.1/src/` | OpenAI-compatible API evaluation pipeline. |
| `v1.1/training/` | Data conversion script and LLaMA-Factory Qwen3-32B LoRA configurations. |

## Data Statistics

| Item | Count |
| --- | ---: |
| Raw train review samples | 1,216 |
| Raw test review samples | 414 |
| Grouped train surveys | 480 |
| Grouped test surveys | 163 |
| Cleaned article full texts | 1,646 |

Each grouped record contains a survey identifier, title and abstract, review text, source, multilingual metadata where available, and a list of dimension-level scores and reviewer rationales. The article shards map each survey identifier to its cleaned full text.

The evaluator uses scores in `{-2, -1, 1, 2}`. Unlabeled or non-evaluated entries with score `0` or `-3` are skipped by the provided evaluation and training utilities.

## Download v1.1

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="brighterrluo/SurveyReview",
    repo_type="dataset",
    allow_patterns=["v1.1/**"],
    local_dir="SurveyReview",
)
```

## Run the v1.1 Evaluator

```bash
cd SurveyReview/v1.1
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env
```

Set `API_KEY`, `BASE_URL`, `MODEL_NAME`, and `JUDGE_MODEL` in `.env`, then run:

```bash
python src/api_base_evaluate.py
```

The evaluator uses the test split by default. Set `EVAL_SPLIT=train` to evaluate the training split, or set `EVALUATE_REASONS=False` to skip judge-model rationale scoring.

## v1.0-paper Resources

The archived `v1.0-paper` release includes the original review data, prompts, article data, 1,646 Marker-parsed Markdown articles, and 1,646 Grobid XML articles.

To reconstruct and extract the Marker Markdown archive:

```bash
cat v1.0-paper/markdown_archive/marker_markdown.tar.gz.part-* > marker_markdown.tar.gz
tar -xzf marker_markdown.tar.gz
```

To extract the Grobid XML archive:

```bash
tar -xzf v1.0-paper/grobid_xml_archive/grobid_xml.tar.gz
```

## Citation

If you use SurveyReview in your research, please cite:

```bibtex
@inproceedings{zhang2026surveyreview,
  author = {Zhang, Yuheng and Wang, Yuanchun and Zhang, Fanjin and Zhao, Ruyu and Li, Juanzi and Tang, Jie and Zhang, Jing},
  title = {{SurveyReview}: A Reviewer-Aligned Benchmark for Survey Evaluators},
  booktitle = {Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2},
  year = {2026},
  pages = {10302--10313},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  location = {Jeju Island, Republic of Korea},
  series = {KDD '26},
  isbn = {979-8-4007-2259-2},
  doi = {10.1145/3770855.3817505},
  url = {https://doi.org/10.1145/3770855.3817505}
}
```