Summary of Harmbench: a Standardized Evaluation Framework For Automated Red Teaming and Robust Refusal, by Mantas Mazeika et al.
HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal
by Mantas Mazeika, Long Phan, Xuwang Yin, Andy Zou, Zifan Wang, Norman Mu, Elham Sakhaee, Nathaniel Li, Steven Basart, Bo Li, David Forsyth, Dan Hendrycks
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces HarmBench, a standardized evaluation framework for automated red teaming in large language models (LLMs). This framework aims to address the lack of rigorous assessment methods in the field and provides a comprehensive evaluation of 18 red teaming methods and 33 target LLMs and defenses. The authors identify several desirable properties unaccounted for in previous evaluations and design HarmBench to meet these criteria. The paper also presents a highly efficient adversarial training method that enhances LLM robustness across various attacks, demonstrating the codevelopment of attacks and defenses enabled by HarmBench. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automated red teaming is important because it helps keep language models safe from being misused. This research creates a way to test how well different methods do this job. They call it HarmBench. It makes sure all the testing methods are fair and compare them correctly. The authors use HarmBench to try out 18 ways of doing red teaming and see how they work with many language models and defenses. This helps us understand what works best and how we can make our language models safer. |
