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English(EN) RLMOpt: Adaptive Prompt Optimization via Recursive Language Models

新研究探索用于自动提示优化的模块化和递归方法

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新研究论文介绍了优化大型语言模型提示的新方法。第一篇SAPO将提示分解为角色、上下文和任务等部分，从而进行有针对性的改进，以提高在各种基准测试中的性能。第二篇RLMOpt利用递归语言模型来驱动优化过程本身，从而在多个任务和基准测试中实现更高效、更有效的提示生成，其性能通常优于GEPA等现有方法。 AI

影响 这些新的提示优化技术可能导致在各种应用中更高效、更有效地使用LLM。

排序理由 arXiv上发表了两篇学术论文，详细介绍了提示优化的新方法。

在 arXiv cs.AI 阅读 →

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新研究探索用于自动提示优化的模块化和递归方法

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Nikita Kulin, Viktor Zhuravlev, Artur Khairullin, Sergey Muravyov, Ilya Makarov, Daniil Sukhorukov, Ekaterina Averkova ·

    从整体到模块化：分段级自动提示优化

    arXiv:2608.11219v1 Announce Type: new 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 out…

  2. arXiv cs.AI TIER_1 English(EN) · Subhash Bangalore Satheesha, Nirvik Pande, Deepthi Duddempudi, Bharath Dandala ·

    RLMOpt：通过递归语言模型实现自适应提示优化

    arXiv:2608.10471v1 Announce Type: new Abstract: Prompt optimizers automate the search for prompts that improve language-model performance, but existing methods rely on a predefined optimization procedure: the algorithm determines which candidates to explore and how the search pro…

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