@doc :2026-0307

README.md

@@ -27,41 +27,17 @@ configs:
 
 # AnesBench
 
-[**Project Page**](https://mililab.github.io/anesbench.ai/) | [**Paper**](https://huggingface.co/papers/2504.02404) | [**GitHub**](https://github.com/mililab/anesbench)
+[**Paper**](https://huggingface.co/papers/2504.02404) | [**GitHub**](https://github.com/mililab/anesbench)
 
-**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.
+# Dataset Description
 
-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).
+**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.
 
 | 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`):
@@ -80,6 +56,23 @@ python ./evaluate.py --config ./config.yaml
 }
 ```
 
+**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                                                                 |
@@ -91,6 +84,23 @@ python ./evaluate.py --config ./config.yaml
 | `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 |
+
+## Recommended Usage
+
+- **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.
+
+## Usage
+
+To evaluate a model on AnesBench, you can use the evaluation code provided in the [official repository](https://github.com/MiliLab/AnesSuite).
+
+
 ## Citation
 
 If you find AnesBench helpful, please consider citing the following paper: