Re-Reading Improves Reasoning in Large Language Models

Re-Reading Improves Reasoning in Large Language Models
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Original Paper
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{

Summary Notes

Enhancing Reasoning in LLMs with the Re2 Prompting Method

Large Language Models (LLMs) like ChatGPT have transformed natural language processing. Yet, they often struggle with complex reasoning tasks.
The Re2 prompting method is a breakthrough approach designed to boost LLMs' reasoning abilities, offering a brighter future for AI applications.

Understanding the Re2 Prompting Method

Re2 (Re-Reading) prompting enhances how LLMs understand inputs by allowing them to "re-read" questions for a deeper understanding.
This method is especially effective when combined with thought-eliciting strategies like Chain-of-Thought (CoT) prompting, leading to improved reasoning performance.

Key Features of Re2

  • Easy Integration: Re2's plug-and-play nature makes it compatible with various LLMs and algorithms.
  • Versatility: It works well in zero-shot, few-shot learning, different prompt strategies, and self-consistency settings.

Experimental Insights

Evaluation across 14 datasets showed Re2's ability to improve reasoning in tasks such as arithmetic, commonsense, and symbolic reasoning. Here’s what the results showed:
  • Arithmetic Reasoning: Enhanced performance in complex calculations.
  • Commonsense Reasoning: Better understanding and reasoning in everyday scenarios.
  • Symbolic Reasoning: Improved capabilities in tasks requiring symbolic thought.

Looking Forward

While Re2 shows promise, future work aims to optimize re-read numbers, test more prompt strategies, and explore its effectiveness in new settings like multi-turn dialogue and multi-modal reasoning.

Challenges and Ethical Considerations

The study notes limitations like a focus on empirical rather than theoretical validation and potential efficiency drops with longer questions. Ethically, the research upheld rigorous standards, ensuring no personal information was used.


The Re2 prompting method is a significant advancement in enhancing LLM reasoning capabilities. Its ease of use, compatibility, and proven effectiveness make it a valuable asset for AI Engineers. As research progresses, Re2's full potential in various AI applications remains an exciting prospect.

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