In-Context Prompt Optimization With Multi-Phase Mutation Search
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Solution Overview
Problem
Conventional prompt engineering for large language models is manual, time-consuming, requires significant human effort, and often results in suboptimal performance due to the separate optimization of instructions and examples, lacking computational efficiency.
Innovation Solution
An automated prompt optimization system employing multi-phase mutation, including global initialization, local feedback, global evolution, and local semantic mutations, to jointly optimize prompts, leveraging LLMs for gradient information and evolutionary algorithms to enhance search efficiency and convergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual prompt engineering is used, then prompts can be customized for specific tasks, but the process requires considerable time and effort
Solution Approach 1:
The system performs self-service by automatically generating and optimizing prompts without human intervention. The automated prompt optimization system uses multi-phase mutation and evolutionary algorithms to autonomously create high-performance prompts, eliminating the need for manual prompt engineering while maintaining or improving prompt quality.
Solution Approach 2:
The patent replaces the manual mechanical process of prompt engineering with an automated computational system. Instead of human experts manually crafting prompts, the system uses algorithms including multi-phase mutation, local feedback mutation, global evolution mutation, and semantic mutation to automatically generate optimized prompts.
2Reliability
If manual prompt tuning is performed to achieve improved performance, then prompt effectiveness increases, but significant human resources and expert knowledge are required
Solution Approach 1:
The system autonomously performs prompt optimization without requiring human experts. It uses automated evaluation metrics and evolutionary algorithms to self-adjust and improve prompts, replacing the need for human resources and expert knowledge in the prompt tuning process.
Solution Approach 2:
The system optimizes prompt parameters automatically through multi-phase mutation operations including semantic mutation, local feedback mutation, and global evolution mutation. These parameter changes are driven by automated fitness evaluation rather than human judgment, reducing dependency on expert knowledge.
3Ease of manufacture
If separate optimization of instructions and examples is performed, then each component can be tuned independently, but the overall prompt performance becomes suboptimal
Solution Approach 1:
The patent merges the optimization of instructions and examples into a unified process. The multi-phase mutation system simultaneously optimizes both components together, evaluating their combined effectiveness through fitness functions and adjusting them as an integrated prompt structure rather than separate elements.
Solution Approach 2:
The automated optimization system performs multiple functions simultaneously: it generates candidate prompts, evaluates fitness, applies various mutation operations (semantic, local feedback, global evolution), and selects optimal prompts. This multi-functional approach handles both instructions and examples uniformly throughout the optimization process.
4Productivity
If automated prompt generation is implemented, then efficiency increases, but balancing exploration and exploitation becomes challenging
Solution Approach 1:
The patent segments the optimization process into distinct phases: semantic mutation phase, local feedback mutation phase, and global evolution mutation phase. Each phase focuses on specific aspects of optimization (semantic diversity, local refinement, global search), making the complex process more manageable and controllable while maintaining overall efficiency.
Solution Approach 2:
The system maintains continuous optimization through iterative multi-phase mutation cycles. Rather than discrete separate steps, the phases continue sequentially and can repeat, with each phase building on previous results. This continuous action ensures both exploration and exploitation occur throughout the optimization process without interruption.
Data Source
AI summary
Certain aspects of the disclosure provide unified in-context prompt optimization for large language models that achieves joint optimization of prompt instruction and examples. A multi-phase approach is provided that includes multiple mutation operations. Further, the approach alternates between optimization strategies for exploration for global search and exploitation for local search. Global initialization creates a diverse set of candidate prompts based on the availability of data and utilizing Lamarckian or semantic mutation. Local feedback mutation, global evolution mutation, and local semantic mutation can subsequently be employed iteratively to generate a revised set of candidate prompts. A prompt from the revised set of candidate prompts can be selected based on an evaluation of the candidate prompts. Subsequently, the selected prompt can be output for a machine-learning task.