用大模型自动修正异常地址，让快递少跑冤枉路。
AddrLLM: Address Rewriting via Large Language Model on Nationwide Logistics Data
- 基于检索增强的大模型框架，自动识别并修正错误地址
- 线上部署后使包裹重派率降低约43%
- 适合物流、导航等需要精准地址的场景
地理位置的文字描述（即地址）在即时配送、导航等位置服务中至关重要。然而，大量包含误差的异常地址导致定位不准，造成显著成本。地址重写成为修复异常地址的有效方案。现有方法多针对特定错误类型，且常需重新训练以适应新数据。本文提出AddrLLM，一种基于检索增强大语言模型的创新地址重写框架，通过监督微调模块、面向地址的检索增强生成模块和无偏目标对齐模块，克服了上述局限。据我们所知，这是首个将基于大模型的地址重写应用于全国规模真实数据的研究。通过离线测试与线上部署验证，AddrLLM在现有物流系统中表现优异，使包裹重派率降低约43%，展现出卓越的实际应用效果。
原文摘要 · Abstract (English)
Textual description of a physical location, commonly known as an address, plays an important role in location-based services(LBS) such as on-demand delivery and navigation. However, the prevalence of abnormal addresses, those containing inaccuracies that fail to pinpoint a location, have led to significant costs. Address rewriting has emerged as a solution to rectify these abnormal addresses. Despite the critical need, existing address rewriting methods are limited, typically tailored to correct specific error types, or frequently require retraining to process new address data effectively. In this study, we introduce AddrLLM, an innovative framework for address rewriting that is built upon a retrieval augmented large language model. AddrLLM overcomes aforementioned limitations through a meticulously designed Supervised Fine-Tuning module, an Address-centric Retrieval Augmented Generation module and a Bias-free Objective Alignment module. To the best of our knowledge, this study pioneers the application of LLM-based address rewriting approach to solve the issue of abnormal addresses. Through comprehensive offline testing with real-world data on a national scale and subsequent online deployment, AddrLLM has demonstrated superior performance in integration with existing logistics system. It has significantly decreased the rate of parcel re-routing by approximately 43\%, underscoring its exceptional efficacy in real-world applications.
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