Enhancing Address Data Integrity using Transformer-Based Language Models Dönüştürücü Tabanlı Dil Modelleri Kullanarak Adres Veri Bütünlüğünün Geliştirilmesi
32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024, Mersin, Türkiye, 15 - 18 Mayıs 2024, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/siu61531.2024.10601149
- Basıldığı Şehir: Mersin
- Basıldığı Ülke: Türkiye
- Anahtar Kelimeler: Address Standardization, Fine-Tuning, Synthetic Data, Transformers, Turkish Address Dataset
- Orta Doğu Teknik Üniversitesi Adresli: Evet
Özet
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.