Enhancing Address Data Integrity using Transformer-Based Language Models Dönüştürücü Tabanlı Dil Modelleri Kullanarak Adres Veri Bütünlüğünün Geliştirilmesi

Date

2024-01-01

Author

Kürklü, Ömer Faruk
Akagündüz, Erdem

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Address data integrity is a critical aspect in numerous applications, yet it is often plagued with inaccuracies and inconsistencies, particularly in non-standardized formats. This study explores a novel application of transformer-based language models, traditionally utilized in language translation tasks, for the standardization and correction of Turkish address data. Leveraging the capabilities of Mixtral-8x7B, a state-of-the-art large language model, this research introduces a unique, handcrafted dataset of Turkish addresses. This dataset, derived from the National Address Dataset and enriched through ChatGPT-4 to simulate human-like input errors.This dataset was later used in fine-tuning both TowerInstruct and T5 models, transforming them into tools capable of converting faulty, error-laden address lines into standardized, structured, and corrected formats.

Subject Keywords

Address Standardization, Fine-Tuning, Synthetic Data, Transformers, Turkish Address Dataset

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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85200859936&origin=inward
https://hdl.handle.net/11511/110721

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Graduate School of Informatics, Conference / Seminar