---
id: 20260528-T0-11
title: "结构化输出逼小模型“犯错”：约束越强，正确率越低"
title_en: "Strict Constraints Degrade Accuracy in Small Model Outputs"
url: https://ai.daily.yangsir.net/daily/20260528-T0-11
issue_date: 2026-05-28
publish_date: 2026-05-27T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2605.26128
---

# 结构化输出逼小模型“犯错”：约束越强，正确率越低

生产环境中的LLM系统经常需要输出JSON或符合正则表达式的结构化数据。然而，论文发现对于3B参数以下的小语言模型（SLM），强制要求格式合规会明显降低内容的准确性。研究量化了这种“约束税”，即输出有效性与正确率之间的权衡。开发者在使用轻量级本地模型生成机器可读代码或API调用时，必须在格式约束和逻辑准确性之间做出谨慎的平衡。

## English Version

**Strict Constraints Degrade Accuracy in Small Model Outputs**

Forcing strict structured outputs (like JSON) on small language models under 3B parameters significantly reduces their factual accuracy. The paper quantifies this 'constraint tax.' Developers using local SLMs for tool calls must carefully balance format compliance with logical correctness.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2605.26128)

**详情页**：https://ai.daily.yangsir.net/daily/20260528-T0-11

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