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Emergent chain-of-thought (CoT) reasoning capabilities promise to improveperformance and explainability of large language models (LLMs). However,uncertainties remain about how reasoning strategies formulated for previousmodel generations generalize to new model generations and different datasets.In this small-scale study, we compare different reasoning strategies induced byzero-shot prompting across six recently released LLMs (davinci-002,davinci-003, GPT-3.5-turbo, GPT-4, Flan-T5-xxl and Cohere command-xlarge) on amixture of six question-answering datasets, including datasets from scientificand medical domains. Our findings demonstrate that while some variations ineffectiveness occur, gains from CoT reasoning strategies remain robust acrossdifferent models and datasets. GPT-4 has the most benefit from currentstate-of-the-art reasoning strategies and exhibits the best performance byapplying a prompt previously discovered through automated discovery.

An automatically discovered chain-of-thought prompt generalizes to novel models and datasets