iPOE: Interpretable Prompt Optimization via Explanations
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866911724194496512 |
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| author | Li, Jiahui Resendiz, Yarik Menchaca Papay, Sean Klinger, Roman |
| author_facet | Li, Jiahui Resendiz, Yarik Menchaca Papay, Sean Klinger, Roman |
| contents | Prompt optimization has often been framed as a discrete search problem to find high-performing and robust instructions for an LLM. However, the search result might not make it transparent why and where specific prompt changes lead to performance gains. This is in contrast to how humans are instructed for annotation tasks. Here, researchers carefully design annotation guidelines, leading to enhanced annotation consistency. Our paper aims at joining these two approaches and introduces iPOE, a novel interpretable prompt optimization strategy via explanations. We guide the prompt optimization process by automatically created guidelines from explanations of annotation decisions (either automatically generated or from humans). This set of guidelines is furthermore optimized by as series of operations, including removing, adding, shuffling, and merging. The resulting prompt includes guidelines that instruct the annotation, making the decision process of the LLM and the optimization transparent. It therefore supports also laypeople in the area of prompt optimization, particularly in challenging domains requiring expertise. In our experiments on four datasets, we find that iPOE can improves over the evaluated baselines by up to 39% and LLM explanations can replace human explanations in the proposed method. Moreover, our interpretability validation study demonstrates that humans and LLMs can substantially agree on which guidelines contribute to their annotations, achieving a Cohen's kappa score of up to 0.65. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_18113 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | iPOE: Interpretable Prompt Optimization via Explanations Li, Jiahui Resendiz, Yarik Menchaca Papay, Sean Klinger, Roman Computation and Language Prompt optimization has often been framed as a discrete search problem to find high-performing and robust instructions for an LLM. However, the search result might not make it transparent why and where specific prompt changes lead to performance gains. This is in contrast to how humans are instructed for annotation tasks. Here, researchers carefully design annotation guidelines, leading to enhanced annotation consistency. Our paper aims at joining these two approaches and introduces iPOE, a novel interpretable prompt optimization strategy via explanations. We guide the prompt optimization process by automatically created guidelines from explanations of annotation decisions (either automatically generated or from humans). This set of guidelines is furthermore optimized by as series of operations, including removing, adding, shuffling, and merging. The resulting prompt includes guidelines that instruct the annotation, making the decision process of the LLM and the optimization transparent. It therefore supports also laypeople in the area of prompt optimization, particularly in challenging domains requiring expertise. In our experiments on four datasets, we find that iPOE can improves over the evaluated baselines by up to 39% and LLM explanations can replace human explanations in the proposed method. Moreover, our interpretability validation study demonstrates that humans and LLMs can substantially agree on which guidelines contribute to their annotations, achieving a Cohen's kappa score of up to 0.65. |
| title | iPOE: Interpretable Prompt Optimization via Explanations |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2605.18113 |