Improving Language Model Performance by Allowing Them to Rephrase and Respond to Questions

Core Concepts

Allowing large language models to rephrase and expand on questions before responding can significantly improve their performance across a wide range of reasoning tasks.

Abstract

The paper presents a method called "Rephrase and Respond" (RaR) that allows large language models (LLMs) to rephrase and expand on questions posed by humans, and then provide responses in a single prompt. This approach aims to address the disparity between human and LLM thought frames, which can lead to LLMs misinterpreting seemingly unambiguous questions.
The key insights and findings are:

LLMs can exhibit their own frames of thought that differ from those of humans, leading to unexpected interpretations of questions. This is demonstrated through examples where LLMs like GPT-4 provide incorrect responses due to ambiguities in the original questions.

The RaR method, where the LLM first rephrases and expands on the question before responding, consistently improves the performance of various LLMs, including GPT-4, GPT-3.5, and Vicuna, across a diverse set of reasoning tasks.

Variations of the RaR prompt, with slightly different wording, also remain effective, indicating the robustness of the approach.

More advanced LLMs, such as GPT-4, benefit the most from the RaR method, while less complex models like Vicuna see more modest improvements.

The paper introduces a two-step variant of RaR, where a rephrasing LLM first generates a clarified question, which is then used by a responding LLM. This allows stronger LLMs to assist weaker ones in improving question comprehension.

The RaR method is shown to be complementary to the Chain-of-Thought (CoT) prompting technique, and the two can be combined to achieve even better performance.

Overall, the paper demonstrates that allowing LLMs to rephrase and expand on questions can be a simple yet effective way to enhance their reasoning capabilities in the zero-shot setting.

Stats

"The last letter of 'Elon' is 'n'. The last letter of 'Musk' is 'k'. When you put these letters together, the result is 'nk'."
"The last letter of 'Annette' is 'e'. The last letter of 'Erin' is 'n'. The last letter of 'Marisol' is 'l'. The last letter of 'Esperanza' is 'a'. Concatenating them is 'enla'."
"The first letter of 'Elon' is 'E'. The first letter of 'Musk' is 'M'. Concatenating them is 'EM'."

Quotes

"The last letter of 'Elon' is 'n'. The last letter of 'Musk' is 'k'. When you put these letters together, the result is 'nk'."
"The last letter of 'Annette' is 'e'. The last letter of 'Erin' is 'n'. The last letter of 'Marisol' is 'l'. The last letter of 'Esperanza' is 'a'. Concatenating them is 'enla'."
"The first letter of 'Elon' is 'E'. The first letter of 'Musk' is 'M'. Concatenating them is 'EM'."

Key Insights Distilled From

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

by Yihe Deng,We... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2311.04205.pdf

Deeper Inquiries

How can the RaR method be extended to handle more complex question types, such as those involving logical reasoning or multi-step problem-solving?

The RaR method can be extended to handle more complex question types by incorporating specific prompts that guide the LLM through logical reasoning or multi-step problem-solving processes. For questions involving logical reasoning, the prompt can include instructions to identify key logical operators, premises, and conclusions, prompting the LLM to rephrase the question in a logical format before responding. This approach helps the LLM break down the question into logical components, enhancing its ability to reason effectively.
Similarly, for multi-step problem-solving questions, the RaR method can guide the LLM through each step of the problem-solving process. The prompt can include cues to identify the different components of the problem, such as variables, constraints, and objectives. The LLM can then rephrase the question to clarify each step before providing a comprehensive response that addresses all parts of the problem.
By structuring the prompts in a way that aligns with the specific requirements of logical reasoning or multi-step problem-solving, the RaR method can effectively support the LLM in handling more complex question types. Additionally, providing feedback mechanisms to evaluate the LLM's reasoning process and adjust the prompts accordingly can further enhance its performance in these challenging tasks.

What are the potential limitations of the RaR approach, and how could it be further improved to address them?

One potential limitation of the RaR approach is the reliance on the quality of the rephrased questions generated by the LLM. If the rephrased questions do not accurately capture the essence of the original question or introduce new ambiguities, it can lead to incorrect responses. To address this limitation, incorporating a feedback loop where human evaluators review and provide feedback on the rephrased questions can help improve their quality over time.
Another limitation is the scalability of the RaR method to handle a large volume of questions across diverse domains. To address this, automated techniques such as leveraging pre-trained models for question rephrasing or implementing a self-improvement mechanism where the LLM learns from its own rephrasing mistakes can enhance the scalability and efficiency of the RaR approach.
Furthermore, the RaR method may face challenges in handling highly specialized or domain-specific questions that require domain knowledge beyond the LLM's training data. To overcome this limitation, integrating domain-specific knowledge bases or expert systems to assist the LLM in rephrasing and responding to such questions can enhance its performance in specialized domains.

How might the insights from this work on aligning human and LLM thought frames be applied to other areas of human-AI interaction, such as task planning or open-ended dialogue?

The insights from aligning human and LLM thought frames can be applied to other areas of human-AI interaction to improve collaboration and communication between humans and AI systems. In task planning, understanding the discrepancies in how humans and LLMs interpret instructions can help in designing prompts and feedback mechanisms that facilitate clearer communication and more effective task execution. By aligning the thought frames of humans and LLMs, task planning systems can enhance efficiency and accuracy in task completion.
In open-ended dialogue, the insights from this work can inform the design of conversational agents that better understand and respond to human queries. By incorporating prompts that encourage the LLM to rephrase and clarify ambiguous questions, dialogue systems can provide more accurate and contextually relevant responses. This approach can enhance the naturalness and coherence of conversations between humans and AI systems, leading to more engaging and effective interactions.
Overall, applying the principles of aligning human and LLM thought frames to task planning and open-ended dialogue can enhance the overall user experience and utility of AI systems in various interactive settings.