ScholarQABench

Comprehensive Assessment Platform for Scientific Literature

CommonProductOthersScientific LiteratureEvaluation Platform

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.

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.

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.

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.

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.

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ScholarQABench Visit Over Time

Monthly Visits

515580771

Bounce Rate

37.20%

Page per Visit

5.8

Visit Duration

00:06:42

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