Microsoft recently released PromptBench, a new tool library for evaluating large language models. PromptBench supports a variety of models and tasks, provides standard, dynamic and semantic evaluation methods, and includes a variety of prompt engineering methods and adversarial testing capabilities. It also supports a variety of data sets and models, and provides tools such as visual analysis and word frequency analysis to interpret evaluation results. The simple and easy-to-use interface allows researchers to quickly build models, load data sets, and evaluate model performance for comprehensive performance testing and analysis. This is a powerful tool that will significantly improve the efficiency and accuracy of large language model evaluation.
Microsoft recently released the PromptBench tool library designed for evaluating large language models. The tool library supports a variety of models and tasks, provides standard, dynamic and semantic evaluation methods, and integrates multiple hint engineering methods and adversarial testing. It supports a variety of data sets and models, and provides tools for interpreting evaluation results such as visual analysis and word frequency analysis. PromptBench's simple interface allows you to quickly build models, load datasets, and evaluate model performance, providing researchers with comprehensive performance testing and analysis support.
The release of PromptBench provides a more efficient and comprehensive tool for the evaluation of large language models. It is believed that it will help promote the continued development and innovation in the field of large language models and provide stronger support for researchers and developers. Its convenient operation and rich functionality make it ideal for evaluating large language models.