Datasets:

zai-org
/

SurveyReview

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

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 for setup, evaluation, and benchmark results.

Versions

Version	Status	Description
`v1.1/`	Latest	Cleaned article texts, XML-style rationale/score prompts, evaluation code, and training recipes.
`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

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

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:

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:

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:

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

Citation

If you use SurveyReview in your research, please cite:

@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}
}