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--- |
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language: |
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- en |
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tags: |
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- medical |
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--- |
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# MMedS-Bench |
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[💻Github Repo](https://github.com/MAGIC-AI4Med/MedS-Ins) [🖨️arXiv Paper](https://arxiv.org/abs/2408.12547) |
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The official benchmark for "Towards Evaluating and Building Versatile Large Language Models for Medicine". |
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## Introduction |
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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. |
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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: |
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- Multi-choice Question Answering: Tests the ability of LLMs to select correct answers from multiple options based on clinical knowledge. |
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- Text Summarization: Assesses the capability to concisely summarize medical texts. |
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- Information Extraction: Evaluates how effectively an LLM can identify and extract relevant information from complex medical documents. |
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- Explanation and Rationale: Requires the model to provide detailed explanations or justifications for clinical decisions or data. |
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- Named Entity Recognition: Focuses on the ability to detect and classify entities within a medical text. |
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- Diagnosis: Tests diagnostic skills, requiring the LLM to identify diseases or conditions from symptoms and case histories. |
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- Treatment Planning: Involves generating appropriate treatment plans based on patient information. |
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- Clinical Outcome Prediction: Assesses the ability to predict patient outcomes based on clinical data. |
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- Text Classification: Involves categorizing text into predefined medical categories. |
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- Fact Verification: Tests the ability to verify the accuracy of medical facts. |
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- Natural Language Inference: Requires deducing logical relationships from medical text. |
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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。 |
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## Data Format |
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The data format is the same as [MedS-Ins](https://huggingface.co/datasets/Henrychur/MedS-Ins). |
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```bash |
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{ |
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"Contributors": [""], |
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"Source": [""], |
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"URL": [""], |
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"Categories": [""], |
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"Reasoning": [""], |
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"Definition": [""], |
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"Input_language": [""], |
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"Output_language": [""], |
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"Instruction_language": [""], |
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"Domains": [""], |
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"Positive Examples": [ { "input": "", "output": "", "explanation": ""} ], |
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"Negative Examples": [ { "input": "", "output": "", "explanation": ""} ], |
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"Instances": [ { "id": "", "input": "", "output": [""]} ], |
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} |
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``` |
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