Learning from Prompt itself: the Hierarchical Attribution Prompt Optimization

• URL: http://arxiv.org/abs/2601.02683v1
• Date: Tue, 06 Jan 2026 03:34:17 GMT
• Title: Learning from Prompt itself: the Hierarchical Attribution Prompt Optimization
• Authors: Dongyu Chen, Jian Ma, Xianpeng Zhang, Lei Zhang, Haonan Lu, Chen Chen, Chuangchuang Wang, Kai Tang,
• Abstract summary: A structured optimization approach requires automated or semi-automated procedures to develop improved prompts.<n>Current prompt optimization methods often induce prompt drift, where new prompts fix prior failures but impair performance on previously successful tasks.<n>This study proposes the Hierarchical Prompt Optimization framework, which introduces three innovations: (1) a dynamic attribution mechanism targeting error patterns in training data and prompting history, (2) semantic-unit optimization for editing functional prompt segments, and (3) multimodal-friendly progression supporting both end-to-end LLM and LLM-MLLM.
• Score: 13.8868879878572
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Optimization is fundamental across numerous disciplines, typically following an iterative process of refining an initial solution to enhance performance. This principle is equally critical in prompt engineering, where designing effective prompts for large language models constitutes a complex optimization challenge. A structured optimization approach requires automated or semi-automated procedures to develop improved prompts, thereby reducing manual effort, improving performance, and yielding an interpretable process. However, current prompt optimization methods often induce prompt drift, where new prompts fix prior failures but impair performance on previously successful tasks. Additionally, generating prompts from scratch can compromise interpretability. To address these limitations, this study proposes the Hierarchical Attribution Prompt Optimization (HAPO) framework, which introduces three innovations: (1) a dynamic attribution mechanism targeting error patterns in training data and prompting history, (2) semantic-unit optimization for editing functional prompt segments, and (3) multimodal-friendly progression supporting both end-to-end LLM and LLM-MLLM workflows. Applied in contexts like single/multi-image QA (e.g., OCRV2) and complex task analysis (e.g., BBH), HAPO demonstrates enhanced optimization efficiency, outperforming comparable automated prompt optimization methods and establishing an extensible paradigm for scalable prompt engineering.

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