Prompt Optimization with Human Feedback

Xiaoqiang Lin, Zhongxiang Dai, Arun Verma, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low·May 27, 2024

Summary

This paper investigates the problem of optimizing prompts for large language models (LLMs) without numeric performance scores, focusing on scenarios where human feedback is the primary evaluation method. The authors propose Automated Prompt Optimization with Human Feedback (APOHF), a dueling bandits-inspired algorithm that efficiently selects prompt pairs for user preferences. APOHF is applied to tasks such as improving user instructions, text-to-image models, and refining responses, showing its effectiveness in finding high-performing prompts with minimal feedback. The study demonstrates APOHF's superiority over baselines in tasks like optimizing user instructions and response generation, even with limited data. The algorithm's practicality is highlighted, as it addresses real-world challenges where direct performance evaluation is not feasible, and the potential for misuse is acknowledged, calling for future research on security measures.

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper "Prompt Optimization with Human Feedback" addresses the problem of prompt optimization using human preference feedback for large language models (LLMs) . This problem involves optimizing the input prompt for a black-box LLM by obtaining preference feedback from a human user, who selects the preferred response from a pair of prompts . The paper introduces an algorithm named automated POHF (APOHF) that selects a pair of prompts to query for preference feedback in each iteration, aiming to efficiently find a good prompt with minimal human feedback instances . This problem is not entirely new, as previous works have focused on prompt optimization but often required numeric scores to assess prompt quality, which can be challenging to obtain in interactions with black-box LLMs . The novelty of this paper lies in its approach of using human preference feedback exclusively for prompt optimization, making it more feasible and reliable in scenarios where obtaining numeric scores is impractical .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to prompt optimization with human feedback. The research focuses on developing algorithms that utilize human feedback to optimize prompts for large language models (LLMs) . The goal is to improve the performance and productivity of LLMs by training them to follow instructions effectively based on human preferences and feedback . The study explores the theoretical justifications and practical implications of prompt selection strategies, such as maximizing predicted scores and upper confidence bounds, inspired by previous works on linear dueling bandits . Additionally, the paper discusses the potential societal impacts of the algorithm, including the risk of malicious users providing misleading feedback to LLMs for inappropriate tasks .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Prompt Optimization with Human Feedback" introduces several innovative ideas, methods, and models in the field of optimizing prompts for large language models (LLMs) using human feedback . Here are some key contributions outlined in the paper:

  1. Clip-tuning: The paper proposes the concept of "Clip-tuning," which aims at derivative-free prompt learning by utilizing a mixture of rewards .

  2. RLHF Deciphered: It critically analyzes reinforcement learning from human feedback for LLMs, providing insights into the effectiveness and limitations of this approach .

  3. InstructZero: The paper introduces "InstructZero," an efficient instruction optimization method tailored for black-box large language models .

  4. Prompt Rewriting with Reinforcement Learning: PRewrite is presented as a method for prompt rewriting using reinforcement learning techniques .

  5. Prefix-Tuning: The paper discusses "Prefix-Tuning," a strategy for optimizing continuous prompts for generation tasks .

  6. Use Your INSTINCT: This model focuses on instruction optimization using neural bandits coupled with transformers .

  7. LiPO: Listwise Preference Optimization through Learning-to-Rank is introduced as a method for optimizing preferences in a listwise manner .

  8. PromptAgent: Strategic planning with language models enables expert-level prompt optimization through the PromptAgent model .

  9. Localized Zeroth-Order Prompt Optimization: This method emphasizes zeroth-order optimization for prompts in a localized context .

  10. Alpacafarm: A simulation framework for methods that learn from human feedback, providing a platform for testing and evaluating such approaches .

These novel ideas, methods, and models contribute to advancing the field of prompt optimization with human feedback, offering diverse strategies for enhancing the performance and effectiveness of large language models through optimized prompts. The paper "Prompt Optimization with Human Feedback" introduces several novel characteristics and advantages compared to previous methods in the field of prompt optimization for large language models (LLMs) using human feedback. Here are some key points highlighted in the paper with references to specific details:

  1. Clip-tuning Approach: The paper proposes the "Clip-tuning" method, which focuses on derivative-free prompt learning by incorporating a mixture of rewards . This approach aims to enhance prompt optimization by leveraging a combination of reward signals without relying on gradients, offering a unique strategy compared to traditional optimization methods.

  2. Theoretical Justifications: The paper provides theoretical justifications for the prompt selection strategy employed in the APOHF algorithm, demonstrating a principled approach inspired by linear dueling bandits . By maximizing predicted scores and utilizing upper confidence bounds, the APOHF algorithm strategically selects prompts based on a weighted combination of score predictions and uncertainty terms, showcasing a well-founded prompt selection strategy.

  3. Effectiveness Verification: The effectiveness of the prompt selection strategy is further validated through experiments where the APOHF strategy is compared to uniform random prompt selection while keeping other components fixed . This verification process underscores the efficacy of the APOHF algorithm in selecting prompts that lead to improved outcomes in various tasks.

  4. Innovative Prompt Optimization Methods: The paper introduces innovative methods such as "InstructZero," "INSTINCT," and "ZOPO" that offer advancements in prompt optimization for LLMs . These methods leverage techniques like Bayesian optimization, neural bandits, and zeroth-order optimization to enhance prompt learning and improve model performance.

  5. Alignment with Human Values: The RLHF method, widely used for aligning LLM responses with human values, is discussed in the paper, highlighting the importance of directly incorporating human preference feedback for model alignment . This approach sidesteps the need for reinforcement learning and directly utilizes preference datasets for alignment, showcasing a more direct and efficient way to optimize prompts based on human feedback.

By incorporating these characteristics and advancements, the paper contributes significantly to the field of prompt optimization with human feedback, offering principled strategies, innovative methods, and effective prompt selection approaches that enhance the performance and alignment of large language models with human preferences.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research works exist in the field of prompt optimization with human feedback. Noteworthy researchers in this area include A. Amini, T. Vieira, R. Cotterell , Y. Dubois, C. X. Li, R. Taori, T. Zhang, I. Gulrajani, J. Ba, C. Guestrin, P. S. Liang, T. B. Hashimoto , and Xiaoqiang Lin, Zhongxiang Dai, Arun Verma, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low .

The key to the solution mentioned in the paper "Prompt Optimization with Human Feedback" is the development of the automated POHF (APOHF) algorithm. This algorithm is designed to optimize prompts for black-box Large Language Models (LLMs) using only human preference feedback. APOHF selects a pair of prompts to query for preference feedback in each iteration based on a theoretically principled strategy inspired by dueling bandits. By leveraging human preference feedback, APOHF efficiently finds a good prompt for various tasks with a small number of feedback instances .


How were the experiments in the paper designed?

The experiments in the paper "Prompt Optimization with Human Feedback" were designed to test the performance of the APOHF algorithm in three sets of tasks:

  1. Optimization of user instructions
  2. Prompt optimization for text-to-image generative models
  3. Response optimization with human feedback .

The experiments involved comparing the APOHF algorithm with three natural baseline methods adapted to POHF:

  1. Random Search, which randomly selects a prompt in every iteration and ignores preference feedback
  2. Linear Dueling Bandits, which uses a linear function to model the latent score function and selects prompts based on a strategy from previous works
  3. Double Thompson Sampling (DoubleTS), which selects prompts using Thompson sampling and Epistemic NNs to model reward uncertainty .

The APOHF algorithm was tested using different tasks and compared against these baseline methods to evaluate its efficiency in solving the problem of POHF .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the MIT License dataset for optimizing user instructions and the Anthropic Helpfulness and Harmlessness datasets for response optimization . The code for the study is not explicitly mentioned to be open source in the provided context.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified. The paper outlines a theoretically principled prompt selection strategy that is rigorously tested through experiments . The experiments demonstrate the effectiveness of the prompt selection strategy by comparing it to a random prompt selection strategy while keeping other components fixed, showing the superiority of the proposed approach . Additionally, the paper provides theoretical justifications for the prompt selection strategy, aligning with previous works on linear dueling bandits and demonstrating a modified version of the algorithm that is well-analyzed . These analyses and experimental results collectively contribute to validating the scientific hypotheses put forth in the paper regarding prompt optimization with human feedback .


What are the contributions of this paper?

The paper "Prompt Optimization with Human Feedback" makes several key contributions in the field of prompt optimization with human feedback:

  • Introduction of Automated POHF Algorithm: The paper introduces the Automated Prompt Optimization with Human Feedback (APOHF) algorithm, which is designed to optimize prompts for black-box Large Language Models (LLMs) using only human preference feedback .
  • Theoretical Foundation: Drawing inspiration from dueling bandits, the paper presents a theoretically principled strategy for selecting a pair of prompts to query for preference feedback in each iteration of the optimization process .
  • Application to Various Tasks: The APOHF algorithm is applied to different tasks, including optimizing user instructions, prompt optimization for text-to-image generative models, and response optimization with human feedback .
  • Efficient Prompt Optimization: Results from the study demonstrate that the APOHF algorithm can efficiently identify a suitable prompt with a minimal number of preference feedback instances, showcasing its effectiveness in prompt optimization with human feedback .

What work can be continued in depth?

Further research can be conducted to develop effective safeguarding methods to prevent potential malicious use of algorithms by malicious users who could intentionally provide misleading preference feedback to language models . Additionally, future work can focus on tackling the limitation of the Automated Prompt Optimization with Human Feedback (APOHF) algorithm, which currently does not accommodate scenarios where more than 2 prompts are selected in every iteration and the user provides feedback regarding the ranking of the responses from these prompts . This could involve developing novel and theoretically principled strategies to choose more than 2 prompts to query, enhancing the algorithm's flexibility and usability in real-world applications.


Introduction
Background
Importance of human evaluation in LLMs
Challenges with numeric performance scores
Objective
To address human feedback-based optimization
Develop APOHF algorithm for efficient prompt selection
Method
Automated Prompt Optimization with Human Feedback (APOHF)
Algorithm Design
Dueling bandits framework
Prompt pair selection mechanism
Implementation
Selection process for user preference queries
Iterative learning and adaptation
Data Collection
Human feedback collection methods
Sample size and data collection strategy
Data Preprocessing
Cleaning and formatting of user feedback
Conversion to preference signals for the algorithm
Experiments and Results
Task Applications
User instructions improvement
Case studies and results
Text-to-image models
Performance enhancement
Response generation refinement
Effectiveness with limited feedback
Comparison with Baselines
APOHF vs. alternative optimization techniques
Statistical significance of results
Evaluation and Discussion
Superiority of APOHF
Real-world applicability
Performance under limited data scenarios
Limitations and Future Work
Addressing potential misuse
Security measures and ethical considerations
Open research questions
Conclusion
Summary of APOHF's achievements
Implications for future LLM prompt optimization
Call for collaboration and further research
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
How does APOHF obtain feedback for prompt optimization in the study?
What are some tasks APOHF is applied to demonstrate its effectiveness?
What is the main advantage of APOHF over baseline methods mentioned in the paper?
What is the primary focus of the paper concerning large language models?

Prompt Optimization with Human Feedback

Xiaoqiang Lin, Zhongxiang Dai, Arun Verma, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low·May 27, 2024

Summary

This paper investigates the problem of optimizing prompts for large language models (LLMs) without numeric performance scores, focusing on scenarios where human feedback is the primary evaluation method. The authors propose Automated Prompt Optimization with Human Feedback (APOHF), a dueling bandits-inspired algorithm that efficiently selects prompt pairs for user preferences. APOHF is applied to tasks such as improving user instructions, text-to-image models, and refining responses, showing its effectiveness in finding high-performing prompts with minimal feedback. The study demonstrates APOHF's superiority over baselines in tasks like optimizing user instructions and response generation, even with limited data. The algorithm's practicality is highlighted, as it addresses real-world challenges where direct performance evaluation is not feasible, and the potential for misuse is acknowledged, calling for future research on security measures.
Mind map
Iterative learning and adaptation
Selection process for user preference queries
Prompt pair selection mechanism
Dueling bandits framework
Open research questions
Security measures and ethical considerations
Addressing potential misuse
Performance under limited data scenarios
Real-world applicability
Statistical significance of results
APOHF vs. alternative optimization techniques
Effectiveness with limited feedback
Response generation refinement
Performance enhancement
Text-to-image models
Case studies and results
User instructions improvement
Conversion to preference signals for the algorithm
Cleaning and formatting of user feedback
Sample size and data collection strategy
Human feedback collection methods
Implementation
Algorithm Design
Develop APOHF algorithm for efficient prompt selection
To address human feedback-based optimization
Challenges with numeric performance scores
Importance of human evaluation in LLMs
Call for collaboration and further research
Implications for future LLM prompt optimization
Summary of APOHF's achievements
Limitations and Future Work
Superiority of APOHF
Comparison with Baselines
Task Applications
Data Preprocessing
Data Collection
Automated Prompt Optimization with Human Feedback (APOHF)
Objective
Background
Conclusion
Evaluation and Discussion
Experiments and Results
Method
Introduction
Outline
Introduction
Background
Importance of human evaluation in LLMs
Challenges with numeric performance scores
Objective
To address human feedback-based optimization
Develop APOHF algorithm for efficient prompt selection
Method
Automated Prompt Optimization with Human Feedback (APOHF)
Algorithm Design
Dueling bandits framework
Prompt pair selection mechanism
Implementation
Selection process for user preference queries
Iterative learning and adaptation
Data Collection
Human feedback collection methods
Sample size and data collection strategy
Data Preprocessing
Cleaning and formatting of user feedback
Conversion to preference signals for the algorithm
Experiments and Results
Task Applications
User instructions improvement
Case studies and results
Text-to-image models
Performance enhancement
Response generation refinement
Effectiveness with limited feedback
Comparison with Baselines
APOHF vs. alternative optimization techniques
Statistical significance of results
Evaluation and Discussion
Superiority of APOHF
Real-world applicability
Performance under limited data scenarios
Limitations and Future Work
Addressing potential misuse
Security measures and ethical considerations
Open research questions
Conclusion
Summary of APOHF's achievements
Implications for future LLM prompt optimization
Call for collaboration and further research

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper "Prompt Optimization with Human Feedback" addresses the problem of prompt optimization using human preference feedback for large language models (LLMs) . This problem involves optimizing the input prompt for a black-box LLM by obtaining preference feedback from a human user, who selects the preferred response from a pair of prompts . The paper introduces an algorithm named automated POHF (APOHF) that selects a pair of prompts to query for preference feedback in each iteration, aiming to efficiently find a good prompt with minimal human feedback instances . This problem is not entirely new, as previous works have focused on prompt optimization but often required numeric scores to assess prompt quality, which can be challenging to obtain in interactions with black-box LLMs . The novelty of this paper lies in its approach of using human preference feedback exclusively for prompt optimization, making it more feasible and reliable in scenarios where obtaining numeric scores is impractical .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to prompt optimization with human feedback. The research focuses on developing algorithms that utilize human feedback to optimize prompts for large language models (LLMs) . The goal is to improve the performance and productivity of LLMs by training them to follow instructions effectively based on human preferences and feedback . The study explores the theoretical justifications and practical implications of prompt selection strategies, such as maximizing predicted scores and upper confidence bounds, inspired by previous works on linear dueling bandits . Additionally, the paper discusses the potential societal impacts of the algorithm, including the risk of malicious users providing misleading feedback to LLMs for inappropriate tasks .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Prompt Optimization with Human Feedback" introduces several innovative ideas, methods, and models in the field of optimizing prompts for large language models (LLMs) using human feedback . Here are some key contributions outlined in the paper:

  1. Clip-tuning: The paper proposes the concept of "Clip-tuning," which aims at derivative-free prompt learning by utilizing a mixture of rewards .

  2. RLHF Deciphered: It critically analyzes reinforcement learning from human feedback for LLMs, providing insights into the effectiveness and limitations of this approach .

  3. InstructZero: The paper introduces "InstructZero," an efficient instruction optimization method tailored for black-box large language models .

  4. Prompt Rewriting with Reinforcement Learning: PRewrite is presented as a method for prompt rewriting using reinforcement learning techniques .

  5. Prefix-Tuning: The paper discusses "Prefix-Tuning," a strategy for optimizing continuous prompts for generation tasks .

  6. Use Your INSTINCT: This model focuses on instruction optimization using neural bandits coupled with transformers .

  7. LiPO: Listwise Preference Optimization through Learning-to-Rank is introduced as a method for optimizing preferences in a listwise manner .

  8. PromptAgent: Strategic planning with language models enables expert-level prompt optimization through the PromptAgent model .

  9. Localized Zeroth-Order Prompt Optimization: This method emphasizes zeroth-order optimization for prompts in a localized context .

  10. Alpacafarm: A simulation framework for methods that learn from human feedback, providing a platform for testing and evaluating such approaches .

These novel ideas, methods, and models contribute to advancing the field of prompt optimization with human feedback, offering diverse strategies for enhancing the performance and effectiveness of large language models through optimized prompts. The paper "Prompt Optimization with Human Feedback" introduces several novel characteristics and advantages compared to previous methods in the field of prompt optimization for large language models (LLMs) using human feedback. Here are some key points highlighted in the paper with references to specific details:

  1. Clip-tuning Approach: The paper proposes the "Clip-tuning" method, which focuses on derivative-free prompt learning by incorporating a mixture of rewards . This approach aims to enhance prompt optimization by leveraging a combination of reward signals without relying on gradients, offering a unique strategy compared to traditional optimization methods.

  2. Theoretical Justifications: The paper provides theoretical justifications for the prompt selection strategy employed in the APOHF algorithm, demonstrating a principled approach inspired by linear dueling bandits . By maximizing predicted scores and utilizing upper confidence bounds, the APOHF algorithm strategically selects prompts based on a weighted combination of score predictions and uncertainty terms, showcasing a well-founded prompt selection strategy.

  3. Effectiveness Verification: The effectiveness of the prompt selection strategy is further validated through experiments where the APOHF strategy is compared to uniform random prompt selection while keeping other components fixed . This verification process underscores the efficacy of the APOHF algorithm in selecting prompts that lead to improved outcomes in various tasks.

  4. Innovative Prompt Optimization Methods: The paper introduces innovative methods such as "InstructZero," "INSTINCT," and "ZOPO" that offer advancements in prompt optimization for LLMs . These methods leverage techniques like Bayesian optimization, neural bandits, and zeroth-order optimization to enhance prompt learning and improve model performance.

  5. Alignment with Human Values: The RLHF method, widely used for aligning LLM responses with human values, is discussed in the paper, highlighting the importance of directly incorporating human preference feedback for model alignment . This approach sidesteps the need for reinforcement learning and directly utilizes preference datasets for alignment, showcasing a more direct and efficient way to optimize prompts based on human feedback.

By incorporating these characteristics and advancements, the paper contributes significantly to the field of prompt optimization with human feedback, offering principled strategies, innovative methods, and effective prompt selection approaches that enhance the performance and alignment of large language models with human preferences.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research works exist in the field of prompt optimization with human feedback. Noteworthy researchers in this area include A. Amini, T. Vieira, R. Cotterell , Y. Dubois, C. X. Li, R. Taori, T. Zhang, I. Gulrajani, J. Ba, C. Guestrin, P. S. Liang, T. B. Hashimoto , and Xiaoqiang Lin, Zhongxiang Dai, Arun Verma, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low .

The key to the solution mentioned in the paper "Prompt Optimization with Human Feedback" is the development of the automated POHF (APOHF) algorithm. This algorithm is designed to optimize prompts for black-box Large Language Models (LLMs) using only human preference feedback. APOHF selects a pair of prompts to query for preference feedback in each iteration based on a theoretically principled strategy inspired by dueling bandits. By leveraging human preference feedback, APOHF efficiently finds a good prompt for various tasks with a small number of feedback instances .


How were the experiments in the paper designed?

The experiments in the paper "Prompt Optimization with Human Feedback" were designed to test the performance of the APOHF algorithm in three sets of tasks:

  1. Optimization of user instructions
  2. Prompt optimization for text-to-image generative models
  3. Response optimization with human feedback .

The experiments involved comparing the APOHF algorithm with three natural baseline methods adapted to POHF:

  1. Random Search, which randomly selects a prompt in every iteration and ignores preference feedback
  2. Linear Dueling Bandits, which uses a linear function to model the latent score function and selects prompts based on a strategy from previous works
  3. Double Thompson Sampling (DoubleTS), which selects prompts using Thompson sampling and Epistemic NNs to model reward uncertainty .

The APOHF algorithm was tested using different tasks and compared against these baseline methods to evaluate its efficiency in solving the problem of POHF .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the MIT License dataset for optimizing user instructions and the Anthropic Helpfulness and Harmlessness datasets for response optimization . The code for the study is not explicitly mentioned to be open source in the provided context.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified. The paper outlines a theoretically principled prompt selection strategy that is rigorously tested through experiments . The experiments demonstrate the effectiveness of the prompt selection strategy by comparing it to a random prompt selection strategy while keeping other components fixed, showing the superiority of the proposed approach . Additionally, the paper provides theoretical justifications for the prompt selection strategy, aligning with previous works on linear dueling bandits and demonstrating a modified version of the algorithm that is well-analyzed . These analyses and experimental results collectively contribute to validating the scientific hypotheses put forth in the paper regarding prompt optimization with human feedback .


What are the contributions of this paper?

The paper "Prompt Optimization with Human Feedback" makes several key contributions in the field of prompt optimization with human feedback:

  • Introduction of Automated POHF Algorithm: The paper introduces the Automated Prompt Optimization with Human Feedback (APOHF) algorithm, which is designed to optimize prompts for black-box Large Language Models (LLMs) using only human preference feedback .
  • Theoretical Foundation: Drawing inspiration from dueling bandits, the paper presents a theoretically principled strategy for selecting a pair of prompts to query for preference feedback in each iteration of the optimization process .
  • Application to Various Tasks: The APOHF algorithm is applied to different tasks, including optimizing user instructions, prompt optimization for text-to-image generative models, and response optimization with human feedback .
  • Efficient Prompt Optimization: Results from the study demonstrate that the APOHF algorithm can efficiently identify a suitable prompt with a minimal number of preference feedback instances, showcasing its effectiveness in prompt optimization with human feedback .

What work can be continued in depth?

Further research can be conducted to develop effective safeguarding methods to prevent potential malicious use of algorithms by malicious users who could intentionally provide misleading preference feedback to language models . Additionally, future work can focus on tackling the limitation of the Automated Prompt Optimization with Human Feedback (APOHF) algorithm, which currently does not accommodate scenarios where more than 2 prompts are selected in every iteration and the user provides feedback regarding the ranking of the responses from these prompts . This could involve developing novel and theoretically principled strategies to choose more than 2 prompts to query, enhancing the algorithm's flexibility and usability in real-world applications.

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