Optimizing Prompts for Large Language Models in Complex Multi-Step Tasks: Integrating Human Feedback and Preference Alignment

Core Concepts

A novel framework called PROMST that integrates human feedback and a learned score prediction model to efficiently optimize prompts for large language models in complex multi-step tasks, outperforming existing methods.

Abstract

The paper introduces PROMST, a framework for optimizing prompts for large language models (LLMs) in complex multi-step tasks. The key insights are:

Prompt optimization for multi-step tasks is challenging due to the complexity of prompts, difficulty in evaluating individual actions, and varied human preferences.

PROMST addresses these challenges by:

Incorporating human-designed feedback rules to automatically provide context to the prompt generation LLM about errors encountered during task execution.
Using a learned score prediction model to efficiently sample and evaluate prompt candidates, reducing the computational cost.
Allowing modification of the task score function to better align with human preferences.

Experiments on 11 diverse multi-step tasks show that PROMST outperforms several state-of-the-art prompt optimization methods by 10.6%-29.3% on average across different LLMs.

The paper also demonstrates that the optimized prompts can generalize to different LLM types, though each LLM performs best with prompts optimized for it specifically.

The learned score prediction model is shown to be effective at filtering out low-performing prompt candidates, improving the overall optimization efficiency.

Ablation studies confirm the importance of both the human feedback rules and the score prediction model in PROMST's superior performance.

The paper also explores how modifying the task score function can help align the optimized prompts with human preferences.

Stats

The average score of the initial human-designed prompts is 0.13.
PROMST achieves an average score of 0.32 across the 11 tasks, outperforming the strongest baseline method (PromptAgent) by 0.05.
On the Boxlift task, the initial human prompt score is 0.31, while PROMST achieves a score of 0.90.
On the Gridworld1 task, the initial human prompt score is 0.23, while PROMST achieves a score of 0.38.

Quotes

"PROMST is the first to explore automatic prompt optimization in multi-step agent tasks."
"The integration of human feedback and the score model greatly improves the prompt optimization process (10.6%-29.3% relative improvements over all baseline methods across different LLMs)."
"We further show that the human-designed evaluation rules can be used to help align task performance with human preferences."

Key Insights Distilled From

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

by Yongchao Che... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2402.08702.pdf

Deeper Inquiries

How can the human-designed feedback rules be further automated or generalized to reduce the burden on users?

To reduce the burden on users and further automate the human-designed feedback rules, several approaches can be considered:

Automated Error Detection: Implement algorithms that can automatically detect common errors in the task execution without relying solely on human input. This can involve using machine learning models to analyze task outcomes and provide feedback.
Feedback Templates: Develop a set of predefined feedback templates that cover a wide range of potential errors or issues in task execution. Users can then select from these templates, reducing the need for manual feedback generation.
Feedback Classification: Utilize natural language processing techniques to automatically classify user feedback into different categories based on the type of error or issue identified. This can streamline the feedback process and make it more efficient.
Feedback Generation Models: Implement generative models that can produce feedback based on the observed errors during task execution. These models can learn from user feedback patterns and generate relevant suggestions automatically.

How can the optimized prompts be made more interpretable, so that the reasons for their superior performance can be better understood?

To enhance the interpretability of optimized prompts and understand the reasons for their superior performance, the following strategies can be employed:

Prompt Visualization: Develop tools or techniques to visualize the optimized prompts, highlighting key components or structures that contribute to their effectiveness. This can help users understand the prompt design better.
Prompt Analysis: Conduct a detailed analysis of the optimized prompts, breaking them down into individual components and explaining how each component influences the task performance. This can provide insights into the prompt's effectiveness.
Prompt Comparison: Compare the optimized prompts with baseline prompts or human-designed prompts to identify specific differences that lead to improved performance. This comparative analysis can shed light on the key factors driving success.
Prompt Explanation Models: Train machine learning models to explain the rationale behind the optimized prompts' performance. These models can provide insights into the features or patterns in the prompts that contribute to their success.

What other techniques, beyond the score prediction model, could be used to efficiently sample and evaluate prompt candidates in complex multi-step tasks?

In addition to the score prediction model, the following techniques can be utilized to efficiently sample and evaluate prompt candidates in complex multi-step tasks:

Active Learning: Implement active learning strategies to intelligently select which prompt candidates to evaluate based on their potential to improve performance. This can reduce the number of evaluations needed.
Meta-Learning: Utilize meta-learning techniques to adapt the prompt optimization process to specific task environments, allowing for faster learning and better generalization to new tasks.
Bayesian Optimization: Apply Bayesian optimization methods to efficiently search the space of prompt candidates by leveraging probabilistic models to guide the search towards promising regions.
Ensemble Methods: Combine multiple prompt optimization algorithms or models to leverage their strengths and improve the overall performance. Ensemble methods can enhance the robustness and efficiency of prompt optimization in multi-step tasks.