重塑自動提示優化

Computer Science > Artificial Intelligence arXiv:2608.

11219 (cs) [Submitted on 21 Jul 2026] Title:From Monolithic to Modular: Segment-level Automatic Prompt Optimization Authors:Nikita Kulin, Viktor Zhuravlev, Artur Khairullin, Sergey Muravyov, Ilya Makarov, Daniil Sukhorukov, Ekaterina Averkova View a PDF of the paper titled From Monolithic to Modular: Segment-level Automatic Prompt Optimization, by Nikita Kulin and 6 other authors View PDF HTML (experimental) Abstract:Automatic Prompt Optimization (APO) often rewrites prompts monolithically, which can improve one behavior while degrading others.

We present SAPO, a segment-level APO method that decomposes prompts into role, context, tasks, and output format, then applies targeted improvements based on top-5 and bottom-5 examples.

The optimization loop uses one LLM with static meta-prompts and structured outputs for segmentation, weakness analysis, and candidate generation.

We describe a train/validation protocol and a two-stage generation process: (1) segment-level diagnosis and recommendation extraction, (2) candidate synthesis constrained by weak/strong segment signals.Using the evaluation setup across SQuADv2, TweetEval, XSUM, CommonGen, and GSM8K on GPT-3.

5-Turbo and GPT-4o-mini, SAPO achieves the best average score against Zero-shot and strong APO baselines including APE, OPRO, EvoPrompt, GEPA, and StraGO.

Comments: Accepted at the IJCAI-ECAI 2026 Workshop on Robustifying Generative AI for Reliable, Safe, and Human-Centric Systems (RobustifAI) Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) Cite as: arXiv:2608.11219 [cs.AI] (or arXiv:2608.

11219v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.

11219 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Artur Khairullin [view email] [v1] Tue, 21 Jul 2026 16:02:04 UTC (3,485 KB) Full-text links: Access Paper: View a PDF of the paper titled From Monolithic to Modular: Segment-level Automatic Prompt Optimization, by Nikita Kulin and 6 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.

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