Duplicate from OpenMOSS-Team/SciJudgeBench

Co-authored-by: Tony.Li <lkdhy@users.noreply.huggingface.co>

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README.md

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+---
+language:
+  - en
+license: apache-2.0
+task_categories:
+  - text-classification
+tags:
+  - scientific-evaluation
+  - citation-prediction
+  - preference-learning
+  - arxiv
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: train.jsonl
+      - split: test
+        path: test.jsonl
+      - split: test_ood_iclr
+        path: test_ood_iclr.jsonl
+      - split: test_ood_year
+        path: test_ood_year.jsonl
+size_categories:
+  - 100K<n<1M
+---
+
+# SciJudge Dataset
+
+Training and evaluation data for scientific paper citation prediction, from the paper **[AI Can Learn Scientific Taste](https://arxiv.org/abs/2603.14473)**.
+
+Given two academic papers (title, abstract, publication date), the task is to predict which paper has a higher citation count.
+
+## Dataset Splits
+
+| Split | Examples | Description |
+|-------|----------|-------------|
+| `train` | 720,341 | Training preference pairs from arXiv papers |
+| `test` | 8,830 | Main evaluation set (880 valid pairs × ~10 prompt variations) |
+| `test_ood_iclr` | 611 | Out-of-distribution evaluation on ICLR papers |
+| `test_ood_year` | 514 | Out-of-distribution evaluation on recent (2025) papers |
+
+## Data Format
+
+Each example is a JSON object with the following fields:
+
+### Core Fields (all splits)
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `messages` | list | Conversation in chat format (`system`, `user` roles) |
+| `correct_answer` | str | Ground truth: `"A"` or `"B"` |
+| `paper_a_citations` | int | Citation count of Paper A |
+| `paper_b_citations` | int | Citation count of Paper B |
+| `paper_a_title` | str | Title of Paper A |
+| `paper_b_title` | str | Title of Paper B |
+| `paper_a_date` | str | Publication date of Paper A (YYYY-MM-DD) |
+| `paper_b_date` | str | Publication date of Paper B (YYYY-MM-DD) |
+| `paper_a_category` | str | Primary category (e.g., "Physics", "Computer Science") |
+| `paper_b_category` | str | Primary category |
+| `paper_a_subcategory` | str | arXiv subcategory (e.g., "cs.CL cs.LG") |
+| `paper_b_subcategory` | str | arXiv subcategory |
+| `paper_a_arxiv_id` | str | arXiv paper ID |
+| `paper_b_arxiv_id` | str | arXiv paper ID |
+| `paper_a_abstract` | str | Abstract of Paper A |
+| `paper_b_abstract` | str | Abstract of Paper B |
+
+### Additional Fields in `test_ood_iclr`
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `paper_a_rating` | float | ICLR review rating of Paper A |
+| `paper_b_rating` | float | ICLR review rating of Paper B |
+| `year` | int | ICLR submission year |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("OpenMOSS-Team/SciJudgeBench")
+
+# Access splits
+train = dataset["train"]
+test = dataset["test"]
+test_iclr = dataset["test_ood_iclr"]
+test_year = dataset["test_ood_year"]
+```
+
+## Citation
+
+```bibtex
+@article{scijudge2025,
+  title={AI Can Learn Scientific Taste},
+  year={2025}
+}
+```

test.jsonl

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train.jsonl

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