Key Points

- Introduction of PromptBench, a unified library designed to evaluate Large Language Models (LLMs) through prompt construction, prompt engineering, dataset and model loading, adversarial prompt attack, dynamic evaluation protocols, and analysis tools.

- Importance of evaluation in understanding LLMs' capabilities, mitigating potential risks, and benefiting society, with a focus on sensitivity to prompts, vulnerability to adversarial prompt attacks, and data contamination.

- Comparison with existing LLM libraries, highlighting the need for a dedicated evaluation framework.

- Components of PromptBench, including supported LLMs, datasets and tasks, prompt types and engineering methods, adversarial prompt attacks, evaluation protocols, and analysis tools.

- Overview of the evaluation pipeline using PromptBench, allowing for task specification, dataset loading, LLM customization, prompt definition, input/output processing, and evaluation function.

- Supported research topics by PromptBench, including benchmarks, scenarios, and protocols, with extensibility and leaderboards for adversarial prompt attack, prompt engineering, and dynamic evaluation.

Summary

The paper introduces PromptBench, a unified library for the evaluation of Large Language Models (LLMs). LLMs have significantly impacted various applications, making their evaluation crucial to understand their performance and mitigate potential security risks. Existing libraries for LLM evaluation lack comprehensive support for various aspects, prompting the development of PromptBench.

Features of PromptBench
PromptBench features a diverse range of LLMs, evaluation datasets, support for various evaluation protocols, adversarial prompt attacks, prompt engineering techniques, and analysis tools. It is designed to be an open, general, and flexible codebase for research purposes, enabling the creation of new benchmarks, downstream applications, and design of new evaluation protocols.

Components of PromptBench
The components of PromptBench include support for various LLMs, diverse tasks, and evaluation datasets, as well as prompt engineering techniques and adversarial prompt attacks. It also supports different evaluation protocols and provides analysis tools for result interpretation. The library's modular design allows researchers to build evaluation pipelines easily.

Research Support and Leaderboards
PromptBench supports research in benchmarking, scenarios, and protocols, and provides leaderboards for adversarial prompt attack, prompt engineering, and dynamic evaluation to facilitate comparison. The paper emphasizes the importance of PromptBench in assessing the true capabilities of LLMs and advancing research in LLM evaluation.

Overall, PromptBench is presented as a step towards understanding LLM capabilities and exploring their boundaries. The paper concludes by highlighting the continuous support and updates for PromptBench and encourages contributions from the research community.

Reference: https://arxiv.org/abs/2312.07910v1