Microsoft Launches Comprehensive Tool Library PromptBench for Large Models
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Microsoft recently introduced an integrated tool library named PromptBench, specifically designed for evaluating large language models. This tool library offers a range of tools, including creating different types of prompts, loading datasets and models, and executing adversarial prompt attacks, to support researchers in evaluating and analyzing LLMs from various perspectives.
Project address: https://github.com/microsoft/promptbench
Paper address: https://arxiv.org/abs/2312.07910
PromptBench's main features and functions include:
Support for multiple models and tasks, enabling evaluation of various large language models such as GPT-4, as well as multiple tasks like sentiment analysis and grammar checking.
Simultaneously, it provides various evaluation methods such as standard assessment, dynamic assessment, and semantic assessment to comprehensively test model performance. Additionally, it implements multiple prompt engineering techniques like few-shot chain-of-thought, emotional prompting, and expert prompting. It also integrates various adversarial testing methods to detect the model's response and resistance to malicious inputs.
Furthermore, it includes analysis tools for interpreting evaluation results, such as visual analysis and word frequency analysis. Most importantly, PromptBench offers an interface that allows for quick model construction, dataset loading, and model performance evaluation. It can be easily installed and used with simple commands, facilitating researchers in building and running evaluation pipelines.
PromptBench supports multiple datasets and models, including GLUE, MMLU, SQuAD V2, IWSLT2017, etc., and numerous models such as GPT-4 and ChatGPT. These features and functionalities make PromptBench a very powerful and comprehensive evaluation toolkit.