AddrLLM: An Innovative Large Language Model Framework for Rewriting Addresses Using Nationwide Logistics Data

المفاهيم الأساسية

AddrLLM, a novel framework leveraging retrieval-augmented large language models, effectively rewrites inaccurate addresses, significantly improving logistics efficiency.

الملخص

• Bibliographic Information: Yang, Q., Hong, Z., Cao, D., Wang, H., Xie, Z., He, T., Liu, Y., Yang, Y., & Zhang, D. (2024). AddrLLM: Address Rewriting via Large Language Model on Nationwide Logistics Data. In Conference’17 (pp. 1–13). ACM. https://doi.org/10.1145/nnnnnnn.nnnnnnn
• Research Objective: This paper introduces AddrLLM, a novel framework utilizing a retrieval-augmented large language model (LLM) to address the pervasive issue of abnormal or inaccurate addresses in logistics, aiming to improve the efficiency of location-based services.
• Methodology: AddrLLM consists of three primary modules: (1) Supervised Fine-Tuning (SFT) trains the LLM on address-related tasks like parsing, entity prediction, and rewriting using a large dataset from JD Logistics. (2) Bias-Free Objective Alignment further refines the LLM using reinforcement learning, with JD's Location-Based Services (LBS) system providing feedback on rewriting accuracy. (3) Address-centric Retrieval Augmented Generation (RAG) enhances the LLM's performance by retrieving relevant addresses based on spatial proximity using a BERT-based retriever fine-tuned on geocoding data.
• Key Findings: Offline experiments demonstrate AddrLLM's superior performance, achieving a 24.2% improvement over state-of-the-art methods in address rewriting tasks. Notably, AddrLLM achieves a 99.9% robustness rate, indicating its ability to identify and preserve correct addresses. In a real-world deployment within JD Logistics in Zhejiang province, AddrLLM significantly reduced parcel re-routing caused by abnormal addresses by approximately 43%.
• Main Conclusions: AddrLLM effectively leverages the power of LLMs and RAG to address the challenges of abnormal addresses in logistics. The framework's robust performance in both offline and online settings highlights its potential to significantly improve the efficiency and accuracy of location-based services.
• Significance: This research pioneers the application of LLM-based address rewriting in real-world logistics, offering a promising solution to a costly and persistent problem. The success of AddrLLM underscores the potential of LLMs in specialized domain tasks and paves the way for future research in applying LLMs to other aspects of logistics and location-based services.
• Limitations and Future Research: The paper acknowledges the computational demands of LLMs and suggests exploring model compression techniques for more efficient deployment. Future research could investigate the generalization of AddrLLM to other languages and address formats, as well as explore its integration with other logistics optimization strategies.

الإحصائيات

JD Logistics faces around 25,000 daily re-routing events caused by abnormal addresses, resulting in annual losses exceeding $2 million.
AddrLLM's address rewriting dataset contains 20 million samples.
The address rewriting dataset includes 77.8% samples that do not involve any rewriting.
AddrLLM reduced station-level inaccuracies by 43% compared to the existing system.
AddrLLM achieves 99.9% robustness in recognizing and preserving correct addresses.
The objective alignment stage operates at an average rate of 13 samples per second.
The retriever in AddrLLM selects the top 10 most relevant addresses from the database.
In online deployment, AddrLLM reduced parcel re-routing by approximately 43%.

اقتباسات

"Abnormal Chinese addresses, defined as those that cannot be parsed into the standard hierarchy... often include errors such as missing administrative regions, nested addresses, unofficial aliases, irrelevant words, and misspellings."
"This issue significantly impacts companies like JD Logistics, one of the largest logistics companies in the world, which faces around 25,000 daily re-routing events caused by abnormal addresses."
"These misrouted parcels, resulting from addresses that dispatch parcels to the wrong delivery stations, lead to additional transfers and re-routing... This process incurs annual losses exceeding $2 million for JD Logistics."
"Empirical evidence suggests that refining user-provided addresses through rewriting... can substantially diminish these errors."

الرؤى الأساسية المستخلصة من

AddrLLM: Address Rewriting via Large Language Model on Nationwide Logistics Data

by Qinchen Yang... في arxiv.org 11-22-2024

https://arxiv.org/pdf/2411.13584.pdf

استفسارات أعمق

How could the insights from AddrLLM's address correction be used to improve address entry systems and minimize errors at the source?

AddrLLM's ability to correct a wide range of address errors presents a valuable opportunity to improve address entry systems proactively. Here's how:

Real-time Address Feedback and Suggestions: Integrating AddrLLM into address entry forms could enable real-time feedback as users type. The model could identify potential errors like missing administrative regions (e.g., city, state), suggest standard formats, or offer auto-completion based on partial addresses. This immediate feedback loop would help users input accurate addresses from the outset.
Error Pattern Analysis and System Refinement: By analyzing the types and frequency of errors AddrLLM corrects, developers can gain insights into common address entry mistakes. This information can be used to refine address entry systems, making them more intuitive and guiding users to input information correctly. For example, if the system frequently detects missing postal codes, it could be redesigned to emphasize the importance of this field.
Personalized Address Books and Profiles: AddrLLM could be used to create more intelligent address books within user accounts or profiles. These address books could learn from past corrections and automatically standardize and store addresses in a consistent format, reducing future errors.
Integration with Geolocation Services: Combining AddrLLM with geolocation services could further enhance address entry accuracy. For example, if a user grants access to their location, the system could cross-reference their input with nearby addresses, improving the accuracy of suggestions and reducing ambiguity.
By implementing these strategies, AddrLLM's insights can be leveraged to create more user-friendly, accurate, and efficient address entry systems, minimizing errors at their source.

Could the reliance on a single company's data (JD Logistics) limit the generalizability and applicability of AddrLLM in diverse global contexts with varying address formats and challenges?

Yes, the reliance on JD Logistics data could potentially limit AddrLLM's generalizability and applicability in diverse global contexts for several reasons:

Address Format Variations: Address formats differ significantly across countries and regions. AddrLLM, trained primarily on Chinese addresses, might not be equipped to handle the nuances of addresses in other countries, such as varying orders of information, postal code systems, or the use of different languages.
Cultural and Linguistic Differences: Language plays a crucial role in address understanding. AddrLLM's training on Chinese addresses might not translate well to languages with different grammatical structures, abbreviations, or address conventions.
Data Bias and Representation: Training data from a single company, even a large one like JD Logistics, might not fully represent the diversity of addresses within a country, let alone globally. The data could be skewed towards specific regions, demographics, or address types, limiting the model's ability to generalize to other contexts.
Address Challenges in Developing Countries: Many developing countries face unique addressing challenges, such as informal settlements with non-standardized addresses or a lack of formal address systems altogether. AddrLLM, trained on data from a company operating in a relatively developed logistics environment, might not be well-suited to address these challenges.
To enhance AddrLLM's generalizability, it's crucial to:

Incorporate Diverse Datasets: Training on address data from multiple sources, including different countries, languages, and address formats, is essential.
Develop Language-Specific Models: Creating language-specific versions of AddrLLM, trained on data and address conventions relevant to those languages, would improve accuracy and applicability.
Address Cultural and Contextual Nuances: The model should be sensitive to cultural and contextual factors that influence addresses, such as variations in abbreviations, landmarks, or informal address systems.

What are the ethical implications of using AI to correct addresses, particularly concerning potential biases in the data and the impact on communities with less standardized addressing systems?

While AI-powered address correction offers efficiency, there are important ethical considerations, especially regarding potential biases and their impact on marginalized communities:

Data Bias Amplification: If the training data reflects existing societal biases (e.g., under-representation of certain neighborhoods or demographics), the AI model might perpetuate and even amplify these biases. This could lead to inaccurate corrections or disadvantages for communities already facing systemic inequities.
Exacerbating Inequalities in Service Delivery: In areas with less standardized addressing, AI models trained on standardized data might struggle to accurately interpret or correct addresses. This could result in delivery errors, delays, or even exclusion from services for residents of these communities, further marginalizing them.
Erosion of Local Knowledge and Practices: Over-reliance on AI-driven address correction could lead to the erosion of local knowledge and informal address systems that function effectively within specific communities. This could have cultural implications and potentially disadvantage individuals who rely on these informal systems.
Privacy Concerns and Data Security: Collecting and using address data raises privacy concerns. It's crucial to ensure data anonymization, secure storage, and transparent data usage policies to protect individuals' privacy and prevent misuse of sensitive information.
To mitigate these ethical risks, it's essential to:

Ensure Data Diversity and Representation: Training data should be carefully curated to represent the diversity of addresses and communities, mitigating bias and ensuring equitable outcomes.
Develop Context-Aware and Inclusive Models: AI models should be designed to be sensitive to different address formats, languages, and cultural contexts, avoiding a one-size-fits-all approach.
Involve Impacted Communities: Engaging with communities that rely on less standardized addressing systems is crucial to understand their needs, address concerns, and ensure the technology benefits them.
Prioritize Transparency and Accountability: Clear explanations of how the AI system works, its limitations, and the data used for training can build trust and enable better understanding of potential impacts.
By addressing these ethical implications proactively, developers can harness the power of AI for address correction while promoting fairness, inclusivity, and respect for diverse communities.