Improve dataset card: add links, citation and evaluation instructions (#2)

- Improve dataset card: add links, citation and evaluation instructions (11fd023bcea4b3f455a4367e859f87f0f4aaf639)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,14 +1,16 @@
 ---
-task_categories:
-- question-answering
 language:
 - en
 - zh
+license: c-uda
+size_categories:
+- 1K<n<10K
+task_categories:
+- question-answering
 tags:
 - biology
 - medical
-size_categories:
-- 1K<n<10K
+- anesthesiology
 viewer: true
 configs:
 - config_name: en
@@ -21,18 +23,45 @@ configs:
   - split: test
     path:
     - anesbench_zh.json
-license: c-uda
 ---
 
-# Dataset Description
+# AnesBench
+
+[**Project Page**](https://mililab.github.io/anesbench.ai/) | [**Paper**](https://huggingface.co/papers/2504.02404) | [**GitHub**](https://github.com/mililab/anesbench)
 
-**AnesBench** is designed to assess anesthesiology-related reasoning capabilities of Large Language Models (LLMs). It provides bilingual (English and Chinese) anesthesiology questions across two separate files. Each question is labeled with a three-level categorization of cognitive demands based on dual-process theory (System 1, System 1.x, and System 2), enabling evaluation of LLMs' knowledge, application, and clinical reasoning abilities across diverse linguistic contexts.
+**AnesBench** is a comprehensive benchmark designed to assess anesthesiology-related reasoning capabilities of Large Language Models (LLMs). It is the evaluation component of **AnesSuite**, the first comprehensive dataset suite specifically designed for anesthesiology reasoning.
+
+The benchmark features 7,972 anesthesiology Multiple Choice Questions (MCQs) available in both English and Chinese. Each question is labeled with a three-level categorization of cognitive demands based on dual-process theory:
+- **System 1**: Factual retrieval (Fast, intuitive recall).
+- **System 1.x**: Hybrid reasoning (Pattern recognition and rule application).
+- **System 2**: Complex decision-making (Deliberate, analytical clinical reasoning).
 
 | Subset | File | Total | System 1 | System 1.x | System 2 |
 |--------|------|-------|----------|-------------|----------|
 | English | `anesbench_en.json` | 4,343 | 2,960 | 1,028 | 355 |
 | Chinese | `anesbench_zh.json` | 3,529 | 2,784 | 534 | 211 |
 
+## Sample Usage
+
+To evaluate a model on AnesBench, you can use the evaluation code provided in the [official repository](https://github.com/mililab/anesbench).
+
+### Setup
+```bash
+git clone https://github.com/MiliLab/AnesBench
+cd AnesBench/eval
+pip install -r requirements.txt
+```
+
+### Run Evaluation
+Prepare your environment variables and run the evaluation script:
+```bash
+export RESULT_SAVE_PATH=/path/to/result_save_dir
+export MODEL_PATH=/path/to/model
+export BENCHMARK_PATH=/path/to/benchmark
+
+python ./evaluate.py --config ./config.yaml 
+```
+
 ## JSON Sample
 
 **English** (`anesbench_en.json`):
@@ -51,23 +80,6 @@ license: c-uda
 }
 ```
 
-**Chinese** (`anesbench_zh.json`):
-
-```json
-{
-    "A": "替代治疗",
-    "B": "手术治疗",
-    "C": "对症治疗",
-    "D": "静脉输注糖皮质激素",
-    "E": "补充盐皮质激素",
-    "id": "78587bd9-f3f6-4118-b6eb-95ed7c91a0ec",
-    "question": "Addison病抢救的主要措施是",
-    "choice_num": 5,
-    "target": "D",
-    "category": 1
-}
-```
-
 ## Field Explanations
 
 | Field         | Type   | Description                                                                 |
@@ -79,14 +91,17 @@ license: c-uda
 | `target`         | string   | The correct answer to this question                                         |
 | `category`       | int      | The cognitive demand category of the question (`1` = System 1, `2` = System 1.x, `3` = System 2) |
 
-### Cognitive Demand Categories
-
-| Category | Label | Description |
-|----------|-------|-------------|
-| 1 | **System 1** | Fast, intuitive recall of factual knowledge |
-| 2 | **System 1.x** | Pattern recognition and application of learned rules |
-| 3 | **System 2** | Deliberate, analytical clinical reasoning |
+## Citation
 
-## Recommended Usage
+If you find AnesBench helpful, please consider citing the following paper:
 
-- **Question Answering**: QA in a zero-shot or few-shot setting, where the question is fed into a QA system. Accuracy should be used as the evaluation metric.
+```latex
+@inproceedings{
+  feng2026anessuite,
+  title={AnesSuite: A Comprehensive Benchmark and Dataset Suite for Anesthesiology Reasoning in {LLM}s},
+  author={Xiang Feng and Wentao Jiang and Zengmao Wang and Yong Luo and Pingbo Xu and Baosheng Yu and Hua Jin and Jing Zhang},
+  booktitle={The Fourteenth International Conference on Learning Representations},
+  year={2026},
+  url={https://openreview.net/forum?id=iKRQMeC7yO}
+}
+```