  To enhance the reasoning capabilities of off-the-shelf Large Language Models
(LLMs), we introduce a simple, yet general and effective prompting method, Re2,
i.e., \textbf{Re}-\textbf{Re}ading the question as input. Unlike most
thought-eliciting prompting methods, such as Chain-of-Thought (CoT), which aim
to elicit the reasoning process in the output, Re2 shifts the focus to the
input by processing questions twice, thereby enhancing the understanding
process. Consequently, Re2 demonstrates strong generality and compatibility
with most thought-eliciting prompting methods, including CoT. Crucially, Re2
facilitates a "bidirectional" encoding in unidirectional decoder-only LLMs
because the first pass could provide global information for the second pass. We
begin with a preliminary empirical study as the foundation of Re2, illustrating
its potential to enable "bidirectional" attention mechanisms. We then evaluate
Re2 on extensive reasoning benchmarks across 14 datasets, spanning 112
experiments, to validate its effectiveness and generality. Our findings
indicate that, with the exception of a few scenarios on vanilla ChatGPT, Re2
consistently enhances the reasoning performance of LLMs through a simple
re-reading strategy. Further analyses reveal Re2's adaptability, showing how it
can be effectively integrated with different LLMs, thought-eliciting prompting,
and ensemble strategies. Our code is available at
\url{https://github.com/Tebmer/Rereading-LLM-Reasoning/}
