No Offense Taken: Eliciting Offensiveness from Language Models

TL;DR

This paper presents a pipeline for automated red teaming of language models to generate offensive test cases, aiming to improve robustness testing of deployed models.

Contribution

It introduces a novel pipeline using smaller language models for automated red teaming to generate offensive test cases and analyze failure modes.

Findings

Generated a diverse corpus of offensive test cases

Identified failure modes in deployed language models

Demonstrated effectiveness of smaller LMs in red teaming

Abstract

This work was completed in May 2022. For safe and reliable deployment of language models in the real world, testing needs to be robust. This robustness can be characterized by the difficulty and diversity of the test cases we evaluate these models on. Limitations in human-in-the-loop test case generation has prompted an advent of automated test case generation approaches. In particular, we focus on Red Teaming Language Models with Language Models by Perez et al.(2022). Our contributions include developing a pipeline for automated test case generation via red teaming that leverages publicly available smaller language models (LMs), experimenting with different target LMs and red classifiers, and generating a corpus of test cases that can help in eliciting offensive responses from widely deployed LMs and identifying their failure modes.