  Large language models (LLMs) have demonstrated emergent capabilities across
diverse reasoning tasks via popular Chains-of-Thought (COT) prompting. However,
such a simple and fast COT approach often encounters limitations in dealing
with complicated problems, while a thorough method, which considers multiple
reasoning pathways and verifies each step carefully, results in slower
inference. This paper addresses the challenge of enabling LLMs to autonomously
select between fast and slow inference methods, thereby optimizing both
efficiency and effectiveness. We introduce a dynamic decision-making framework
that categorizes tasks into two distinct pathways: 'Fast', designated for tasks
where the LLM quickly identifies a high-confidence solution, and 'Slow',
allocated for tasks that the LLM perceives as complex and for which it has low
confidence in immediate solutions as well as requiring more reasoning paths to
verify. Experiments on five popular reasoning benchmarks demonstrated the
superiority of the DynaThink over baselines.
