Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Abstract: Misunderstandings arise not only in interpersonal communication but also between humans and LLMs. Such discrepancies can make LLMs interpret seemingly unambiguous questions in unexpected ways, yielding incorrect responses. While it is widely acknowledged that the quality of a prompt, such as a question, significantly impacts the quality of the response provided by LLMs, a systematic method for crafting questions that LLMs can better comprehend is still underdeveloped. In this paper, we present a method named `Rephrase and Respond' (RaR), which allows LLMs to rephrase and expand questions posed by humans and provide responses in a single prompt. This approach serves as a simple yet effective prompting method for improving performance. We also introduce a two-step variant of RaR, where a rephrasing LLM first rephrases the question and then passes the original and rephrased questions together to a different responding LLM. This facilitates the effective utilization of rephrased questions generated by one LLM with another. Our experiments demonstrate that our methods significantly improve the performance of different models across a wide range to tasks. We further provide a comprehensive comparison between RaR and the popular Chain-of-Thought (CoT) methods, both theoretically and empirically. We show that RaR is complementary to CoT and can be combined with CoT to achieve even better performance. Our work not only contributes to enhancing LLM performance efficiently and effectively but also sheds light on a fair evaluation of LLM capabilities. Data and codes are available at https://github.com/uclaml/Rephrase-and-Respond.

• The paper introduces the novel RaR method, which improves LLM reasoning by letting models rephrase queries before responding.
• One-step and two-step RaR approaches demonstrate enhanced clarity by separating the rephrasing and answering processes.
• Empirical evaluations reveal that RaR outperforms Chain-of-Thought techniques by directly refining question clarity for robust performance gains.

Rephrase and Respond: Let LLMs Ask Better Questions for Themselves

In recent advancements in the domain of artificial intelligence, LLMs have demonstrated significant capabilities across a multitude of tasks. However, challenges persist in aligning LLMs' frames of interpretation with those of humans, potentially leading to unexpected and incorrect responses. This paper by Deng et al. discerns these discrepancies and proposes a novel method, termed "Rephrase and Respond" (RaR), that enhances the performance of LLMs by refining the questions posed to them.

Methodology

The core innovation of the RaR methodology lies in its simplicity and effectiveness: allowing LLMs to rephrase and expand upon questions before providing answers. The authors introduce two variants:

1. One-step RaR: LLMs are prompted to rephrase and respond within a single query, streamlining the process and achieving immediate clarification and response enhancement.
2. Two-step RaR: This involves a rephrasing LLM generating a rephrased version of the question, which, along with the original, is then processed by a responding LLM. This sequential approach allows for leveraging different models' strengths in rephrasing and responding, respectively.

Experimental Evaluation

The efficacy of RaR is backed by empirical evaluations across a diverse array of benchmark tasks. Notably, the application of both One-step and Two-step RaR consistently yielded substantial improvements in LLM performance metrics, highlighting the method's robustness across various reasoning and knowledge tasks.

Moreover, Two-step RaR revealed that rephrased questions crafted by more advanced models, such as GPT-4, could significantly improve the performance of other models, such as Vicuna, indicating the transferability of enhanced question quality.

Comparison with Chain-of-Thought (CoT)

An intriguing comparison is made between RaR and Chain-of-Thought (CoT) methods. Theoretical and empirical insights suggest that while CoT focuses on generating intermediate reasoning steps, RaR directly enhances question clarity. The complementary nature of RaR to CoT methods is evident, and their integration can lead to superior performance gains.

Implications and Future Research

The implications of this research unfold into two major domains:

• Practical Improvements: The RaR methodology can be seamlessly integrated into existing LLM applications, providing an economical and efficient way to enhance response quality without extensive computational overhead.
• Theoretical Insights: By addressing the fundamental issue of aligned comprehension between humans and LLMs, this work opens avenues for further research into understanding LLMs' frames of thought and optimizing prompt design.

Looking ahead, future research may explore deeper integrations of RaR with other AI paradigms and evaluate its potential in novel application areas, such as interactive conversational agents and real-time decision-support systems.

In conclusion, the introduction of RaR as an effective prompting strategy marks a notable step in refining LLM performance, setting a foundation for future advancements in AI-human interaction understanding and model evaluation frameworks.