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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 thedifficulty and diversity of the test cases we evaluate these models on.Limitations in human-in-the-loop test case generation has prompted an advent ofautomated test case generation approaches. In particular, we focus on RedTeaming Language Models with Language Models by Perez et al.(2022). Ourcontributions include developing a pipeline for automated test case generationvia red teaming that leverages publicly available smaller language models(LMs), experimenting with different target LMs and red classifiers, andgenerating a corpus of test cases that can help in eliciting offensiveresponses from widely deployed LMs and identifying their failure modes.

No Offense Taken: Eliciting Offensiveness from Language Models