| --- |
| language: |
| - en |
| - zh |
| license: c-uda |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - question-answering |
| tags: |
| - biology |
| - medical |
| - anesthesiology |
| viewer: true |
| configs: |
| - config_name: en |
| data_files: |
| - split: test |
| path: |
| - anesbench_en.json |
| - config_name: zh |
| data_files: |
| - split: test |
| path: |
| - anesbench_zh.json |
| --- |
| |
| # AnesBench |
|
|
| [**Paper**](https://huggingface.co/papers/2504.02404) | [**GitHub**](https://github.com/mililab/anesbench) |
|
|
| # Dataset Description |
|
|
| **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 | |
|
|
| ## JSON Sample |
|
|
| **English** (`anesbench_en.json`): |
|
|
| ```json |
| { |
| "id": "91b5e145-57f2-5307-99e4-eafd75643de4", |
| "question": "The concentration of a specific gas in solution depends on which of the following?", |
| "A": "Temperature of the solution", |
| "B": "Volume of the system", |
| "C": "Solubility of the specific gas in that solution", |
| "D": "Molecular weight of the gas", |
| "choice_num": 4, |
| "target": "C", |
| "category": 1 |
| } |
| ``` |
|
|
| **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 | |
| |------------------|----------|-----------------------------------------------------------------------------| |
| | `id` | string | A randomly generated ID using UUID | |
| | `question` | string | The question stem | |
| | `A`–`I` | string | Answer options (from `A` up to the total number of options) | |
| | `choice_num` | int | The number of options in this question | |
| | `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: |
|
|
| ```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} |
| } |
| ``` |
