iPOE: Interpretable Prompt Optimization via Explanations

2026-05-18 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A novel interpretable prompt optimization strategy, iPOE (interpretable Prompt Optimization via Explanations), guides the prompt optimization process for Large Language Models (LLMs) by automatically creating guidelines from explanations of annotation decisions. These guidelines, which can be human-generated or automatically derived from LLM explanations, are then optimized through operations such as removing, adding, shuffling, and merging. The resulting prompt incorporates these explicit guidelines, enhancing the transparency of the LLM's decision process and the optimization steps. This approach aims to make prompt optimization more accessible, especially in complex domains requiring specialized expertise. Experiments across four datasets demonstrate that iPOE improves performance by up to 31% over prompts without guidelines and 35% over prompts with randomly selected guidelines, confirming that LLM-generated explanations can effectively substitute human explanations within the method.

Key takeaway

For AI Engineers developing LLM-based annotation systems, iPOE offers a method to significantly improve prompt performance and transparency. By integrating automatically generated, optimized guidelines into your prompts, you can achieve up to 35% better results compared to random guidelines, making the LLM's decision-making process clearer and more robust, especially in specialized domains. Consider implementing iPOE to enhance both the accuracy and interpretability of your LLM applications.

Key insights

iPOE optimizes LLM prompts using automatically generated, interpretable guidelines derived from annotation explanations.

Principles

• Transparency enhances prompt optimization.
• Guidelines improve LLM annotation consistency.

Method

iPOE generates guidelines from annotation explanations, then optimizes them via operations like removing, adding, shuffling, and merging to create interpretable LLM prompts.

In practice

• Use iPOE for complex annotation tasks.
• Leverage LLM explanations to create guidelines.

Topics

• iPOE
• Prompt Optimization
• Interpretable AI
• Large Language Models
• Annotation Guidelines

Best for: Research Scientist, AI Engineer, NLP Engineer, AI Scientist, Prompt Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.