Emergent chain-of-thought (CoT) reasoning capabilities promise to improve performance and explainability of large language models (LLMs). However, uncertainties remain about how prompting strategies formulated for previous model generations generalize to new model generations and different datasets. In this small-scale study we compare the performance of a range of zero-shot prompts for inducing CoT reasoning across six recently released LLMs (davinci-002, davinci-003, GPT-3.5-turbo, GPT-4, Flan-T5-xxl and Cohere command-xlarge) on a mixture of six question-answering datasets, including datasets from scientific and medical domains. We find that a CoT prompt that was previously discovered through automated prompt discovery shows robust performance across experimental conditions and produces best results when applied to the state-of-the-art model GPT-4.

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