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.gitattributes

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README.md

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+---
+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}
+}
+```

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