Create README.md

README.md

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+---
+language:
+- en
+tags:
+- medical
+---
+# MMedS-Bench
+[💻Github Repo](https://github.com/MAGIC-AI4Med/MedS-Ins)   [🖨️arXiv Paper](https://arxiv.org/abs/2408.12547)
+
+The official benchmark for "Towards Evaluating and Building Versatile Large Language Models for Medicine".
+
+
+
+## Introduction
+MedS-Bench is a comprehensive benchmark designed to assess the performance of various large language models (LLMs) in clinical settings. It extends beyond traditional multiple-choice questions to include a wider range of medical tasks, providing a robust framework for evaluating LLM capabilities in healthcare.
+
+The benchmark is structured around 11 high-level clinical task categories, each derived from a collection of 28 existing datasets. These datasets have been reformatted into an instruction-prompted question-answering format, which includes hand-crafted task definitions to guide the LLM in generating responses. The categories included in MedS-Bench are diverse and cover essential aspects of clinical decision-making and data handling:
+
+- Multi-choice Question Answering: Tests the ability of LLMs to select correct answers from multiple options based on clinical knowledge.
+- Text Summarization: Assesses the capability to concisely summarize medical texts.
+- Information Extraction: Evaluates how effectively an LLM can identify and extract relevant information from complex medical documents.
+- Explanation and Rationale: Requires the model to provide detailed explanations or justifications for clinical decisions or data.
+- Named Entity Recognition: Focuses on the ability to detect and classify entities within a medical text.
+- Diagnosis: Tests diagnostic skills, requiring the LLM to identify diseases or conditions from symptoms and case histories.
+- Treatment Planning: Involves generating appropriate treatment plans based on patient information.
+- Clinical Outcome Prediction: Assesses the ability to predict patient outcomes based on clinical data.
+- Text Classification: Involves categorizing text into predefined medical categories.
+- Fact Verification: Tests the ability to verify the accuracy of medical facts.
+- Natural Language Inference: Requires deducing logical relationships from medical text.
+
+Notably, as the evaluation involves commercial models, for example, GPT-4 and Claude 3.5, it is extremely costly to adopt the original large-scale test split. **Therefore, for some benchmarks, we randomly sampling a number of test cases.** The cases used to reeproduce the results in the paper are in [MedS-Bench-SPLIT](https://huggingface.co/datasets/Henrychur/MedS-Bench-SPLIT). For more details, please refer to our paper。
+
+## Data Format
+The data format is the same as [MedS-Ins](https://huggingface.co/datasets/Henrychur/MedS-Ins).
+```bash
+{
+  "Contributors": [""],
+  "Source": [""],
+  "URL": [""],
+  "Categories": [""],
+  "Reasoning": [""],
+  "Definition": [""],
+  "Input_language": [""], 
+  "Output_language": [""],
+  "Instruction_language": [""],  
+  "Domains": [""],    
+  "Positive Examples": [ { "input": "", "output": "",  "explanation": ""} ], 
+  "Negative Examples": [ { "input": "", "output": "",  "explanation": ""} ],
+  "Instances": [ { "id": "", "input": "", "output": [""]} ],
+}
+```
+