Re-Reading Improves Reasoning in Large Language Models (2024)

Abstract: 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., Re-Reading 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.

## introduction in the ever-evolving landscape of artificial intelligence, large language models (llms) have emerged as a keystone of natural language understanding and generation @xcite @xcite . as llms have become more advanced, a key challenge has emerged: teaching them to reason effectively. the ability to reason well is a key aspect of human intelligence, allowing us to infer, deduce, and solve problems. in llms, this skill is crucial for improving their practical use. despite their impressive abilities, llms often have difficulty with reasoning tasks @xcite , urging researchers to explore more strategies to bolster reasoning ability @xcite @xcite . existing research on reasoning has predominantly concentrated on designing diverse thoughteliciting prompting strategies to elicit reasoning processes in the output phase, such as chainof-thought (cot) @xcite , program-aided language model (pal) @xcite , etc. @xcite @xcite . in contrast, scant attention has been paid to the understanding of the input phase. in fact, comprehension is the first step before solving the problem, which is crucially important. however, in the era of generative ai, most llms adopt the decoder-only llms with unidirectional attention, first pass second pass first pass second pass q roger …… have now : ? q roger …… have now : ? q roger …… have now : ? q roger …… have now : ? figure 2 : illustration of the attention distribution in llama-2 by repeating the question as the input (a darker cell indicates higher attention). the region within the red dashed upper triangle demonstrates that every token in the second pass has obvious attention to its later tokens in the first pass. this suggests that re-reading in llms is promising for achieving a "bidirectional" understanding of the question. based on the observation and inspired by the human strategy of re-reading, we present a simple yet effective and general reasoning prompting strategy, re2, i.e., re-reading the question as input (see the illustration in figure 1 ). while our re2 is simple, it offers several advantages for llms' reasoning scenarios. (1) this approach mirrors the human strategy of problem-solving. llms with re2 show potential for a "bidirectional" understanding of questions. (2) repeating questions allows llms to allocate more computational resources to input encoding, similar to "horizontally" increasing the depth of neural networks. (3) re2 emphasizes understanding during the input phase, making it orthogonal to and compatible with most thought-eliciting prompting methods that focus on the output phase, such as cot and pal. to validate the efficacy and generality of re2, we conducted extensive experiments spanning arithmetic, commonsense, and symbolic reasoning tasks across 14 datasets and 112 experiments. the results show that, with the exception of certain scenarios on vanilla chatgpt, our re2 with a simple re-reading strategy consistently enhances the reasoning performance of llms. re2 exhibits versatility across various llms, such as text-davinci-003, chatgpt, llama-2-13b, and llama-2-70b, spanning both instruction finetuning (ift) and non-ift models. we also explore re2 in task settings of zero-shot and fewshot, thought-eliciting prompting methods, and the self-consistency setting, highlighting its generality. ## related work reasoning with large language models. llms represent a significant milestone in the journey towards artificial general intelligence (agi) @xcite . reasoning ability is particularly crucial on the way towards agi, where artificial intelligence needs to act or think like human beings @xcite . in the literature on llms, performing reasoning tasks via interaction in natural language plays a significant role in evaluating an llm, into which academia and industry have been dedicating many endeavors @xcite @xcite . in principle, most works for reasoning with large language models could fall into the paradigm of "chain-of-thought" @xcite , which assists llms in fulfilling com-plex reasoning tasks by generating intermediate steps explicitly. therefore, most of the endeavors are dedicated to improving the basic principle by the following aspects: i) the structure of "chain", e.g., tree @xcite , graph @xcite ; ii) the modality of the chain, e.g., program @xcite ; iii) the reliability of the chain, e.g., self-consistency @xcite , faithful @xcite , retrieval-based verifying @xcite ; and iv) decomposition of the chain, e.g., least-to-most @xcite , decomposed @xcite , plan-tosolve @xcite . in contrast, our simple re-reading strategy for llms is orthogonal to these improvements via a trade-off between the intermediate steps and the query itself. besides, our re-reading strategy is complementary to many previous works by preventing the answer from being derived overwhelmingly from the cot but overlooking the original query. ## conclusion and future works this paper introduces re2, a simple and effective prompting method for llm reasoning that improves performance by "re-reading" the question. by shifting focus to the input phase, re2 operates independently from other thought-eliciting promptings. moreover, it shows promise in fostering bidirectional comprehension of questions in decoder-only llms. our comprehensive experiments cover a wide range of reasoning benchmarks, diverse llm types, various task settings, and compatibility assessments with other prompting methods, validating the efficacy and versatility of re2. our findings encourage the research community to prioritize a deeper understanding of input questions, thereby complementing existing thought-eliciting prompting strategies. future endeavors will aim to explore its versatility in additional contexts beyond reasoning, including multi-turn dialogue and multi-modal reasoning applications.