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Generating a Chain of Thought (CoT) has been shown to consistently improvelarge language model (LLM) performance on a wide range of NLP tasks. However,prior work has mainly focused on logical reasoning tasks (e.g. arithmetic,commonsense QA); it remains unclear whether improvements hold for more diversetypes of reasoning, especially in socially situated contexts. Concretely, weperform a controlled evaluation of zero-shot CoT across two socially sensitivedomains: harmful questions and stereotype benchmarks. We find that zero-shotCoT reasoning in sensitive domains significantly increases a model's likelihoodto produce harmful or undesirable output, with trends holding across differentprompt formats and model variants. Furthermore, we show that harmful CoTsincrease with model size, but decrease with improved instruction following. Ourwork suggests that zero-shot CoT should be used with caution on sociallyimportant tasks, especially when marginalized groups or sensitive topics areinvolved.

On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning