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English(EN) Do Influence Tactics Matter? Investigating Prompt Framing Effects in LLM Code Generation

研究发现：心理影响策略会影响LLM代码生成

作者 PulseAugur 编辑部 · [1 个来源] ·

一项新发表在arXiv上的研究探讨了人类交流中常用的心理影响策略如何影响大型语言模型（LLMs）在代码生成任务中的表现。研究人员将Yukl & Falbe分类法中的八种影响策略改编为提示模板，并在LiveCodeBench和SWE-bench Verified基准上对五种领先的开源LLM进行了测试。研究结果表明，某些提示框架，特别是那些传达紧迫感的提示，与代码正确性和安全性下降有关。 AI

影响 理解提示框架可以带来更可靠、更安全的AI生成代码。

排序理由 关于LLM提示工程技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

研究发现：心理影响策略会影响LLM代码生成

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关于LLM提示工程技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Alex Deaconu, Anubhav Gupta, Manaal Basha, Nicholas Haydu, Gema Rodr\'iguez-P\'erez ·

    影响策略是否重要？探究提示词框架效应对LLM代码生成的影响

    arXiv:2608.11513v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly integrated into software engineering workflows, helping developers write, debug, test, and maintain code. While prompt wording and structure are known to influence model performance, t…

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