用提示工程+规则校验，让大模型精准解析姓名地址字段。
A System for Name and Address Parsing with Large Language Models
- 通过结构化提示与输入归一化，提升生成一致性
- 在真实数据上达成17字段高准确率与稳定置信度
- 无需微调，适合需要可复现性的工业级应用
将非结构化的人名和地址文本转化为结构化数据，仍是大规模信息系统中的关键挑战。传统基于规则和概率的方法在干净输入下表现良好，但在噪声或跨语言场景中失效；而神经网络与大语言模型（LLMs）常缺乏确定性控制和可复现性。本文提出一种提示驱动、以验证为中心的框架，在不进行微调的情况下，将自由文本记录转换为一致的17字段模式。该方法结合输入归一化、结构化提示、约束解码及严格规则验证，在固定实验设置下确保可复现性。在异构真实地址数据上的评估显示，该方法具有高字段级准确率、强模式遵循性及稳定的置信度校准。结果表明，将确定性验证与生成式提示相结合，可提供一种稳健、可解释且可扩展的结构化信息抽取方案，是训练密集型或领域特定模型的实用替代方案。
原文摘要 · Abstract (English)
Reliable transformation of unstructured person and address text into structured data remains a key challenge in large-scale information systems. Traditional rule-based and probabilistic approaches perform well on clean inputs but fail under noisy or multilingual conditions, while neural and large language models (LLMs) often lack deterministic control and reproducibility. This paper introduces a prompt-driven, validation-centered framework that converts free-text records into a consistent 17-field schema without fine-tuning. The method integrates input normalisation, structured prompting, constrained decoding, and strict rule-based validation under fixed experimental settings to ensure reproducibility. Evaluations on heterogeneous real-world address data show high field-level accuracy, strong schema adherence, and stable confidence calibration. The results demonstrate that combining deterministic validation with generative prompting provides a robust, interpretable, and scalable solution for structured information extraction, offering a practical alternative to training-heavy or domain-specific models.
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