ScholarQABench
S
Scholarqabench
Overview :
ScholarQABench is a comprehensive evaluation platform designed to assess large language models (LLMs) in assisting researchers with the synthesis of scientific literature. Originating from the OpenScholar project, it offers a comprehensive evaluation framework comprising various datasets and evaluation scripts to measure models' performances across different scientific domains. The platform's significance lies in its ability to aid researchers and developers in understanding and enhancing the practicality and accuracy of language models in scientific literature research.
Target Users :
The target audience includes researchers, natural language processing developers, and educators who require a tool to assess and enhance the performance of language models in scientific literature research. ScholarQABench provides the necessary datasets and evaluation tools to help them understand the strengths and weaknesses of models, thereby optimizing their design.
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Use Cases
Researchers use ScholarQABench to evaluate the performance of their developed question-answering systems in the field of computer science.
Educators leverage the platform to teach students how to utilize and assess language models in scientific literature research.
Developers employ ScholarQABench to test and improve their models to better serve biomedical research.
Features
Provides evaluation scripts and data for ScholarQABench: Includes datasets and evaluation scripts from multiple fields to test LLMs' ability to synthesize scientific literature.
Supports multiple scientific domains: Datasets from various fields such as computer science, biomedicine, and neuroscience to evaluate model applications in different areas.
Offers detailed evaluation metrics: Encompasses accuracy, citation completeness, etc., for a comprehensive assessment of model performance.
Supports evaluation after model inference: Users can utilize the provided scripts to evaluate the inference results of their models.
Provides answer conversion scripts: Assists users in transforming raw answer files into the required format for evaluation.
Covers evaluations from short to long text generation: Suitable for various types of scientific literature question-answering tasks.
Offers Prometheus evaluation: Used to assess the organization, relevance, and coverage of answers.
How to Use
1. Visit the ScholarQABench GitHub page and clone or download the code.
2. Set up the environment according to the instructions in README.md, including creating a virtual environment and installing dependencies.
3. Download and prepare the required data files, which include test cases and evaluation metrics.
4. Run model inference to generate answer files, ensuring the file format meets evaluation requirements.
5. Use the provided evaluation scripts to assess model performance, including citation accuracy and content relevance.
6. Analyze the evaluation results and optimize model parameters and performance based on feedback.
7. Repeat steps 4-6 until the model performance reaches a satisfactory level.
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