| --- |
| 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} |
| } |
| ``` |
|
|
