Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves
Yihe Deng, Weitong Zhang, Zixiang Chen, Quanquan Gu (University of California, Los Angeles)
This April 2024 paper introduces an approach called "Rephrase and Respond" (RaR) to improve the performance of Large Language Models (LLMs) by addressing misunderstandings that arise between humans and LLMs.
The authors argue that seemingly clear questions to humans may still be misinterpreted by LLMs due to differences in thought frames. RaR prompts the LLM to rephrase and clarify the question before answering it, leading to improved understanding and performance.
Key Findings and Contributions
RaR is a simple yet effective prompting method for improving LLM performance across a wide range of tasks without relying on plugins or code.
Variations of RaR prompts that maintain the principle of rephrasing before responding remain robustly effective.
All LLMs benefit from RaR, with more advanced models showing greater improvements due to their superior rephrasing capabilities.
Two-step RaR, a variation of RaR, allows stronger LLMs to rephrase questions for weaker LLMs, demonstrating the transferability of rephrased question quality across models.
RaR is complementary to Chain-of-Thought (CoT) methods and can be combined with them to achieve even better performance.
Methodology
RaR prompts the LLM to rephrase and expand the question before responding in a single query.
The effectiveness of RaR is evaluated across multiple benchmark tasks, including knowledge classification, knowledge comparison, commonsense reasoning, and symbolic reasoning.
Experiments are conducted on various LLMs, including GPT-4, GPT-3.5, and Vicuna, to assess the consistency of performance improvement across different architectures.
Two-step RaR is introduced, where a stronger LLM rephrases questions for a weaker LLM to respond, showcasing the transferability of rephrased question quality.
RaR is compared with CoT methods, both zero-shot and few-shot, to highlight its complementary nature and potential for combined performance gains.
Application
Process for Creating Prompts Based on the "Rephrase and Respond" Paper
Identify the task or question:
Clearly define the task or question you want the LLM to address.
Ensure that the task or question is specific and well-defined.
Analyse potential misunderstandings:
Consider how the LLM might interpret the question differently from the human intention.
Identify any ambiguities, vague terms, or missing context that could lead to misinterpretation.
Rephrase the question:
Reframe the question to clarify any ambiguities and provide additional context.
Use precise language and avoid vague terms that could be misinterpreted by the LLM.
Expand the question to include relevant details and constraints.
Formulate the RaR prompt:
Use the RaR template to create the prompt, incorporating the rephrased question.
The basic RaR prompt structure is: "{question}"\nRephrase and expand the question, and respond.
You can use variations of the prompt that maintain the principle of rephrasing before responding.
Test and refine:
Test the RaR prompt with the LLM and evaluate its response.
If the response is unsatisfactory or indicates misunderstanding, iterate on steps 3 and 4.
Refine the rephrased question and prompt until the LLM provides an accurate and relevant response.
Consider Two-step RaR (optional):
If you have access to multiple LLMs with different capabilities, consider using Two-step RaR.
Use a stronger LLM to rephrase the question, and then pass the original and rephrased questions to a weaker LLM for the response.
Combine with CoT methods (optional):
If desired, combine the RaR prompt with Chain-of-Thought (CoT) methods.
Append the CoT prompts (e.g., "Let's think step by step.") to the RaR prompt to encourage step-by-step reasoning.
Example 1
Task: Determine if a person was born on an even-numbered day
Original Question
Was Albert Einstein born on an even-numbered day?
Analyse Potential Misunderstandings
The LLM might interpret "even-numbered day" as referring to an even-numbered month or year.
The LLM might not have access to the exact birth date of Albert Einstein.
Rephrase the Question
Albert Einstein was born on March 14, 1879. Was the day of his birth (14) an even number? Please answer with a simple "Yes" or "No".
Formulate the RaR Prompt
Test and Refine
LLM Response: Albert Einstein was born on March 14, 1879. The day of his birth, 14, is an even number.
Yes.
Assessment: The rephrased question and RaR prompt led to an accurate response from the LLM. No further refinement is needed.
By following this process and using the RaR prompting technique, you can create effective prompts that help align the LLM's understanding with the human intention, resulting in more accurate and relevant responses.
Example 2:
Task: Determine if a historical event occurred before or after the year 2000
Original Question
Did the fall of the Berlin Wall happen before or after the year 2000?
Analyse Potential Misunderstandings
The LLM might not have accurate information about the specific date of the fall of the Berlin Wall.
The question does not provide any additional context about the event.
Rephrase the Question
The Berlin Wall, which divided East and West Berlin, fell on November 9, 1989. Given this information, did the fall of the Berlin Wall occur before or after the year 2000? Please answer with "Before 2000" or "After 2000".
Formulate the RaR Prompt
Example 3:
Task: Identify the main theme of a given text
Original Question
What is the main theme of the following text? "The old man and the sea" is a novel about an aging fisherman's struggle with a giant marlin far out in the Gulf Stream off the coast of Cuba.
Analyse Potential Misunderstandings
The LLM might not have read the full novel and may lack context to accurately identify the main theme.
The question does not provide guidance on the desired level of specificity for the main theme.
Rephrase the Question
Given a brief description of the novel "The Old Man and the Sea" by Ernest Hemingway, which states that it is about an aging fisherman's struggle with a giant marlin off the coast of Cuba, identify the main theme of the story. Provide a concise, one-sentence answer that captures the central idea or message of the novel based on this description.
Formulate the RaR Prompt
Example 4:
Task: Determine the meaning of an idiom
Original Question
What does the idiom "kick the bucket" mean?
Analyse Potential Misunderstandings
The LLM might interpret the phrase literally rather than as an idiom.
The question does not specify the desired format of the answer (e.g., a definition or an example sentence).
Rephrase the Question
The phrase "kick the bucket" is an English idiom. In this context, what does "kick the bucket" mean? Please provide a clear, concise definition of the idiomatic meaning without using the idiom itself in your explanation.
Formulate the RaR Prompt
These examples demonstrate how the process of analysing potential misunderstandings, rephrasing the question, and formulating the RaR prompt can be applied to various tasks and question types to elicit more accurate and relevant responses from LLMs.
Analysis
The paper presents a compelling approach to improving LLM performance by addressing the misalignment between human and LLM thought frames.
The RaR method is simple, effective, and widely applicable across various tasks and LLM architectures. The authors provide extensive experimental evidence to support their claims, demonstrating significant performance improvements on multiple benchmark datasets.
The introduction of Two-step RaR is particularly noteworthy, as it showcases the transferability of rephrased question quality across models. This finding opens up new possibilities for leveraging the capabilities of more advanced LLMs to enhance the performance of less sophisticated models.
The paper provides insights into the limitations of zero-shot and few-shot CoT methods, such as hallucination and sensitivity to example quality, and demonstrates how RaR can help mitigate these issues.
References
Language Models and Reasoning
Zhou et al., 2022b; Zhou et al., 2023: Explores how large language models approach human-level prompt engineering capabilities and theory-of-mind assessments.
Kojima et al., 2022: Discusses zero-shot reasoning capabilities of large language models.
Wei et al., 2022; Zhou et al., 2022a: Investigates the chain-of-thought prompting to elicit reasoning in large language models.
Shao et al., 2023: Synthetic prompting generates chain-of-thought demonstrations for LLMs.
Educational and Conversational Agents
Bozkurt, 2023: Discusses the shift to generative AI-powered conversational agents in education.
Lu et al., 2023: Evaluates the mathematical reasoning of foundational models in visual contexts.
Information Extraction and Data Generation
Fortes, 2023: Details on simple dataset generation.
Prasad et al., 2023: Discusses visual grounding of questions for vision-language models.
Bias and Stereotypes in AI
Nadeem et al., 2021: Measures stereotypical bias in pretrained language models.
Technical Aspects and Innovations in AI
Allen-Zhu and Li, 2023: Physics of language models focusing on knowledge manipulation.
Pawelczyk et al., 2023: Discusses in-context unlearning in language models.
Radford et al., 2019: Describes LLMs as unsupervised multitask learners.
Prompt Engineering and Model Training
Saravia, 2022: A guide to prompt engineering for large language models.
White et al., 2023: Discusses a prompt pattern catalog to enhance prompt engineering.
Applications and Evaluations of Language Models
Touvron et al., 2023: Discusses Llama 2, highlighting open foundation and fine-tuned chat models.
Poesia et al., 2022: Focuses on reliable code generation from pre-trained models.
Schick and Schütze, 2021: Utilizes cloze-questions for text classification and natural language inference.
Analysis of Model Limitations and Improvements
Zhang et al., 2023a; Zhang et al., 2023b: Discuss how LLMs' hallucinations can snowball and multimodal chain-of-thought reasoning.
Welleck et al., 2022: Covers generating sequences by learning to self-correct in LLMs.
Research on Model Interpretation and Feedback
Madaan et al., 2023: Discusses iterative refinement with self-feedback for LLMs.
Yang et al., 2022: Explores longer story generation with recursive reprompting and revision.
These references are sorted to provide a clearer understanding of current trends and research in the field of AI, especially focusing on language models, their applications, biases, technical advancements, and the theoretical foundations driving their development.
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