PromptBench: A Unified Library for Evaluating and Understanding Large Language Models
PromptBench is a Pytorch-based Python package designed for the evaluation of Large Language Models (LLMs). It provides user-friendly APIs for researchers to conduct evaluations on LLMs efficiently. Here are some key features and benefits:
Key Features:
- Easy Installation: Install via pip or GitHub for the latest features.
- User-Friendly APIs: Simplifies the process of evaluating existing datasets and LLMs.
- Support for Various Models: Includes support for multi-modal models and various prompt engineering methods.
- Adversarial Attacks: Integrated tools for simulating black-box adversarial prompt attacks to evaluate model robustness.
- Dynamic Evaluation: Implements DyVal for generating evaluation samples on-the-fly with controlled complexity.
- Efficient Multi-Prompt Evaluation: Uses a small sample of data to predict performance on unseen data, reducing error significantly.
Benefits:
- Quick Start: Users can quickly set up and start evaluating models with minimal configuration.
- Extensibility: Users are encouraged to add new models, datasets, and evaluation methods.
- Comprehensive Documentation: Detailed tutorials and examples are provided to help users get familiar with the library.
Highlights:
- Supports a wide range of datasets and models, including open-source and proprietary options.
- Active community contributions and a welcoming environment for enhancements.
For more information, visit the GitHub repository.
