English(EN) Structure Tax: How Structured Output affects LLMs Performance

大语言模型结构化输出性能取决于模式设计，而非结构本身

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

一篇新的arXiv论文挑战了诸如JSON或XML之类的结构化输出格式会损害大语言模型（LLM）性能的观点。研究人员发现，先前被称为“结构化税”的准确性损失并非源于结构本身，而是源于模式的设计。具体而言，根据推理步骤而非答案来排序字段，可以匹配甚至超过自由格式输出的准确性，尤其对于较小的模型而言。这表明优化模式设计是保持大语言模型推理能力的关键。 AI

影响为结构化输出优化模式设计可以提高大语言模型的推理能力，并减少生产部署中的性能下降。

排序理由该集群包含一篇详细介绍大语言模型性能研究结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Vineet Kumar, Kanishka, Bhuvanesh Mandora · 2026-10-09 04:00

结构化税务：结构化输出如何影响大型语言模型性能

arXiv:2610.12056v1 Announce Type: new Abstract: Deploying large language models in production often requires constraining outputs to structured formats such as JSON or XML, and prior work treats the resulting accuracy loss as an inherent `structure tax'. We re-examine this claim …

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结构化税务：结构化输出如何影响大型语言模型性能

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