English(EN) Visible Reasoning Is Not a Universal Optimizer: Persona- and Thinking-Dependent Effects in Analytics Code Generation

研究发现可见推理的有效性因用户画像和模型而异

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

一篇新发表在arXiv上的研究论文探讨了可见的思维链（CoT）提示在分析代码生成中的有效性。研究发现，可见推理并非一种普遍适用的优化策略，其影响取决于用户画像、目标编程语言（SQL或Python）以及特定模型配置等因素。研究表明，推理策略应根据这些变量进行定制，而不是作为默认设置应用。 AI

影响表明代码生成的提示工程需要高度情境化，而不是依赖于通用的最佳实践。

排序理由发表在arXiv上的研究论文，详细介绍了关于LLM提示技术的发现。[lever_c_demoted from research: ic=1 ai=1.0]

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

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arXiv cs.AI TIER_1 English(EN) · Bhawani Shankar Leelar, Pawan Chorasiya, Davin Hill, Robert E. Tillman, Tamer Soliman · 2026-10-09 04:00

可见推理并非通用优化器：分析代码生成中的个性化和思维依赖效应

arXiv:2610.10639v1 Announce Type: cross Abstract: Visible Chain-of-Thought (CoT) is often treated as a broadly useful reasoning instruction, yet analytics code generation combines natural-language ambiguity, schema grounding, target-language constraints, and model-specific infere…