Promptbench: A Unified Library For Evaluation Of Large Language Models

Zhu Kaijie, Zhao Qinlin, Chen Hao, Wang Jindong, Xie Xing. Arxiv 2023

[Paper] [Code]    
Applications Has Code Prompting Reinforcement Learning Security Tools

The evaluation of large language models (LLMs) is crucial to assess their performance and mitigate potential security risks. In this paper, we introduce PromptBench, a unified library to evaluate LLMs. It consists of several key components that are easily used and extended by researchers: prompt construction, prompt engineering, dataset and model loading, adversarial prompt attack, dynamic evaluation protocols, and analysis tools. PromptBench is designed to be an open, general, and flexible codebase for research purposes that can facilitate original study in creating new benchmarks, deploying downstream applications, and designing new evaluation protocols. The code is available at: https://github.com/microsoft/promptbench and will be continuously supported.

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