用大模型做简单任务浪费资源，论文教人该何时不用。
The Plausibility Trap: Using Probabilistic Engines for Deterministic Tasks
- 提出确定性任务决策矩阵，判断何时该用大模型
- 实测发现用大模型做OCR延迟高6.5倍
- 强调数字素养包括知道何时不该用AI
大型语言模型（LLMs）的普及正推动一种新范式：用户便利性优先于计算效率。本文定义了“合理性陷阱”——即人们使用昂贵的概率性引擎处理简单确定性任务（如光学字符识别或基础验证），导致显著资源浪费。通过微基准测试和对OCR与事实核查的案例研究，我们量化了“效率税”：使用大模型进行简单任务带来约6.5倍的延迟增加，并揭示算法迎合风险。为应对这一问题，我们提出工具选择工程与确定性-概率性决策矩阵，帮助开发者判断何时使用生成式AI，以及何时应避免。文章主张教育体系需转型，真正的数字素养不仅在于会使用生成式AI，更在于懂得何时不使用它。
原文摘要 · Abstract (English)
The ubiquity of Large Language Models (LLMs) is driving a paradigm shift where user convenience supersedes computational efficiency. This article defines the "Plausibility Trap": a phenomenon where individuals with access to Artificial Intelligence (AI) models deploy expensive probabilistic engines for simple deterministic tasks-such as Optical Character Recognition (OCR) or basic verification-resulting in significant resource waste. Through micro-benchmarks and case studies on OCR and fact-checking, we quantify the "efficiency tax"-demonstrating a ~6.5x latency penalty-and the risks of algorithmic sycophancy. To counter this, we introduce Tool Selection Engineering and the Deterministic-Probabilistic Decision Matrix, a framework to help developers determine when to use Generative AI and, crucially, when to avoid it. We argue for a curriculum shift, emphasizing that true digital literacy relies not only in knowing how to use Generative AI, but also on knowing when not to use it.
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