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English(EN) How Prompts Move Language Model Behavior: Frames, Salience, and Construal as Semantic Control

研究人员探讨提示如何从语义上控制语言模型行为

本文提出了一个认知-语义框架,用于理解提示如何影响大型语言模型行为。它引入了框架激活、显著性控制和构建选择等概念,以解释提示如何作为语义条件来指导模型的解释和任务构建。研究表明,在自然语言推理和问答等任务中,提示可以改变模型的判断、证据使用和答案组织,这表明应将提示从单纯的性能提升视角转向分析其语义控制能力。 AI

影响 为理解和改进LLM的提示工程技术提供了理论视角。

排序理由 这是一篇发表在arXiv上的研究论文,详细介绍了一种新的提示工程理论框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

研究人员探讨提示如何从语义上控制语言模型行为

本文如何被排名

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这是一篇发表在arXiv上的研究论文,详细介绍了一种新的提示工程理论框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Dongseok Kim, Hyoungsun Choi, Mohamed Jismy Aashik Rasool, Gisung Oh ·

    提示如何影响语言模型行为:框架、显著性和解释作为语义控制

    arXiv:2512.12688v3 Announce Type: replace-cross Abstract: Prompt engineering is widely used to shape large language model behavior, yet it is often treated as a practical heuristic rather than as a form of natural-language control. This paper develops a cognitive-semantic account…