  Without proper safeguards, large language models will readily follow
malicious instructions and generate toxic content. This risk motivates safety
efforts such as red-teaming and large-scale feedback learning, which aim to
make models both helpful and harmless. However, there is a tension between
these two objectives, since harmlessness requires models to refuse to comply
with unsafe prompts, and thus not be helpful. Recent anecdotal evidence
suggests that some models may have struck a poor balance, so that even clearly
safe prompts are refused if they use similar language to unsafe prompts or
mention sensitive topics. In this paper, we introduce a new test suite called
XSTest to identify such eXaggerated Safety behaviours in a systematic way.
XSTest comprises 250 safe prompts across ten prompt types that well-calibrated
models should not refuse to comply with, and 200 unsafe prompts as contrasts
that models, for most applications, should refuse. We describe XSTest's
creation and composition, and then use the test suite to highlight systematic
failure modes in state-of-the-art language models as well as more general
challenges in building safer language models.
