  Emergent chain-of-thought (CoT) reasoning capabilities promise to improve
performance and explainability of large language models (LLMs). However,
uncertainties remain about how reasoning strategies formulated for previous
model generations generalize to new model generations and different datasets.
In this small-scale study, we compare different reasoning strategies induced by
zero-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 a
mixture of six question-answering datasets, including datasets from scientific
and medical domains. Our findings demonstrate that while some variations in
effectiveness occur, gains from CoT reasoning strategies remain robust across
different models and datasets. GPT-4 has the most benefit from current
state-of-the-art reasoning strategies and exhibits the best performance by
applying a prompt previously discovered through automated discovery.
