PRompt Optimization in Multi-Step Tasks (PROMST): Integrating Human Feedback and Heuristic-based Sampling (2402.08702v4)

Abstract: Prompt optimization aims to find the best prompt to a LLM for a given task. LLMs have been successfully used to help find and improve prompt candidates for single-step tasks. However, realistic tasks for agents are multi-step and introduce new challenges: (1) Prompt content is likely to be more extensive and complex, making it more difficult for LLMs to analyze errors, (2) the impact of an individual step is difficult to evaluate, and (3) different people may have varied preferences about task execution. While humans struggle to optimize prompts, they are good at providing feedback about LLM outputs; we therefore introduce a new LLM-driven discrete prompt optimization framework PRompt Optimization in Multi-Step Tasks (PROMST) that incorporates human-designed feedback rules to automatically offer direct suggestions for improvement. We also use an extra learned heuristic model that predicts prompt performance to efficiently sample from prompt candidates. This approach significantly outperforms both human-engineered prompts and several other prompt optimization methods across 11 representative multi-step tasks (an average 10.6\%-29.3\% improvement to current best methods on five LLMs respectively). We believe our work can serve as a benchmark for automatic prompt optimization for LLM-driven multi-step tasks. Datasets and Codes are available at https://github.com/yongchao98/PROMST. Project Page is available at https://yongchao98.github.io/MIT-REALM-PROMST.

Overview of "PRompt Optimization in Multi-Step Tasks (PROMST): Integrating Human Feedback and Preference Alignment"

The paper "PRompt Optimization in Multi-Step Tasks (PROMST): Integrating Human Feedback and Preference Alignment" addresses the ongoing challenge of prompt optimization for LLMs in multi-step tasks. The authors present a novel framework, PROMST, that aims to optimize prompts by integrating human feedback and aligning with user preferences. This work differentiates itself by tackling the complexities inherent in multi-step tasks through a systematic incorporation of heuristic functions and human-guided feedback within an evolutionary algorithm paradigm.

Key Insights and Contributions

1. Framework Overview: PROMST leverages a genetic algorithm approach where the LLM generates new candidate prompts based on an initial prompt and human-designed feedback. The framework utilizes two sets of LLMs: TaskLLM for execution and PromptLLM for generating candidates, ensuring prompt refinement through tailored human feedback.
2. Challenge of Multi-Step Tasks: The paper highlights the significant challenges posed by multi-step tasks, such as the high complexity of prompt content, difficulty in evaluating the impact of individual steps, and varying user preferences. These complexities necessitate advanced optimization strategies beyond simple tuning methods used for single-step tasks.
3. Human Feedback Integration: Recognizing the struggle of LLMs in error analysis and correction in multi-step tasks, the paper focuses on leveraging human expertise in feedback interpretation to refine prompt suggestions. Predefined human feedback rules significantly contribute to optimizing LLM performance by guiding the generation of effective prompts.
4. Performance Improvements: Numerical results demonstrated a substantial improvement over current methods, with an average relative performance increase of 28% across multiple representative tasks using GPT-3.5 and GPT-4 models. This highlights the effectiveness of PROMST in not only fine-tuning the prompts but also ensuring generalizability across different TaskLLMs.
5. Score Prediction Model: To address the high cost of evaluating numerous candidate prompts—a critical bottleneck in prompt optimization for complex tasks—PROMST incorporates a fine-tuned score prediction model. This model acts as a heuristic to filter and prioritize candidate prompts more effectively, thereby improving search efficiency.
6. Preference Alignment with Score Modifications: The paper also explores how modifying scoring rules can better align task execution with human preferences, offering a pathway for customization based on user-specific criteria beyond mere task completion.

Implications and Future Directions

The findings and methodologies introduced in PROMST have several implications for the field of AI:

• Practical Applications: By effectively aligning LLM outputs with predefined user preferences and leveraging a comprehensive feedback mechanism, PROMST promises enhanced utility in domains that require precise multi-step planning and execution, such as automated robotics and complex decision-making systems.
• Future Research Opportunities: The paper establishes a benchmark for future investigations into prompt optimization for tasks requiring sequential decision-making. It opens up avenues for research into more sophisticated feedback mechanisms and the potential automation of feedback using machine-driven models.
• Scalability and Adaptability: While PROMST demonstrates significant efficacy, extending this approach to a broader set of tasks and adapting it for different LLM architectures could enhance its scalability and widen its applicability.

In conclusion, "PRompt Optimization in Multi-Step Tasks (PROMST)" offers a robust approach to optimizing LLM-driven processes by integrating human feedback and aligning prompt development with user preferences. The proposed framework sets a precedent for enhancing the capabilities of LLMs in complex environments, suggesting promising directions for the evolution of intelligent systems capable of tackling intricate multi-step tasks.