Summary of Addrllm: Address Rewriting Via Large Language Model on Nationwide Logistics Data, by Qinchen Yang et al.
AddrLLM: Address Rewriting via Large Language Model on Nationwide Logistics Data
by Qinchen Yang, Zhiqing Hong, Dongjiang Cao, Haotian Wang, Zejun Xie, Tian He, Yunhuai Liu, Yu Yang, Desheng Zhang
First submitted to arxiv on: 17 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces AddrLLM, a novel framework for address rewriting that tackles the issue of abnormal addresses affecting location-based services. Existing methods are limited, often requiring retraining or being tailored to specific error types. The proposed approach leverages a retrieval-augmented large language model (LLM) and incorporates three modules: Supervised Fine-Tuning, Address-centric Retrieval Augmented Generation, and Bias-free Objective Alignment. These modules enable AddrLLM to effectively process new address data and correct various errors. Offline testing with real-world data on a national scale demonstrates superior performance, reducing parcel re-routing by approximately 43%. This innovative approach has the potential to significantly improve location-based services. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Address rewriting is important for location-based services like delivery and navigation. Many addresses are abnormal, making it hard to find the correct location. Existing methods don’t work well, as they’re designed for specific types of errors or need to be retrained. This paper introduces a new approach called AddrLLM that uses large language models to fix abnormal addresses. It has three parts: fine-tuning, generation, and alignment. These parts help AddrLLM correct different kinds of errors and work well with new address data. The approach was tested offline using real-world data on a national scale and showed great results, reducing re-routing by 43%. This could be very useful for location-based services. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Large language model » Retrieval augmented generation » Supervised
