# A System for Name and Address Parsing with Large Language Models

arXiv: https://arxiv.org/abs/2601.18014 · HTML: https://mlsanity.com/paper/2601.18014/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 25 Jan 2026.

## Builds on (most cited first, 8 of 8)

- [KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction](https://mlsanity.com/paper/2403.07969/) (2024)
- [From Text to Insight: Large Language Models for Materials Science Data Extraction](https://mlsanity.com/paper/2407.16867/) (2024)
- [STRUCTSENSE: A Task-Agnostic Agentic Framework for Structured Information Extraction with Human-In-The-Loop Evaluation and Benchmarking](https://mlsanity.com/paper/2507.03674/) (2025)
- [LLM-Based Information Extraction to Support Scientific Literature Research and Publication Workflows](https://mlsanity.com/paper/2510.04749/) (2025)
- [Case Count Metric for Comparative Analysis of Entity Resolution Results](https://mlsanity.com/paper/2601.02824/) (2026)
- [Multinational Address Parsing: A Zero-Shot Evaluation](https://mlsanity.com/paper/2112.04008/) (2021)
- [Fighting crime with Transformers: Empirical analysis of address parsing methods in payment data](https://mlsanity.com/paper/2404.05632/) (2024)
- [Retrieval-Augmented Multi-LLM Ensemble for Industrial Part Specification Extraction](https://mlsanity.com/paper/2601.05266/) (2025)

## Built on by (most cited first, 2 of 2)

- [Household Movement Detection in Mixed-Format Occupancy Data Using LLM-Based Entity Resolution](https://mlsanity.com/paper/2607.21614/) (2026)
- [PrivacyGuard: Source-Linked Sanitization and Locally Resolvable PII References for Cloud GUI Agents](https://mlsanity.com/paper/2610.16778/) (2026)

## Abstract (arXiv)

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.