🤖 AI Summary
This study addresses the challenge of person identity matching across heterogeneous records characterized by linguistic and cultural complexity, diverse naming conventions, and high data noise. To tackle this problem, the authors propose the Structure-Guided Entity Resolution (SGER) framework, which introduces a novel two-stage curriculum fine-tuning strategy: first guiding a large language model to learn the syntactic and semantic structures of personal names, followed by optimizing it for binary entity matching. Evaluated on 50,000 real-world Indian identity record pairs, SGER achieves 99.02% accuracy and an F1 score of 0.994, significantly outperforming few-shot prompting with GPT-4o and single-stage fine-tuning baselines. The method has been deployed on the Dream11 platform, serving over 250 million users, and demonstrates enhanced robustness and precision in multilingual, high-noise entity resolution scenarios.
📝 Abstract
Matching person names across heterogeneous records is a core challenge in entity resolution, especially within linguistically and culturally complex environments. Variations in naming conventions, inconsistent transliteration across scripts, and frequent data entry errors make it difficult to unify user identities, an essential requirement for Know Your Customer (KYC) compliance. While Large Language Models have shown promise in understanding natural language, they often struggle with the structured ambiguity present in such domain-specific settings. This paper introduces Structure-Guided Entity Resolution (SGER), a novel framework that fine-tunes an LLM through a two-phase curriculum. The model is first trained to parse the grammatical and semantic structure of personal names, then optimized for the downstream task of binary entity matching. We evaluate SGER in the challenging context of Indian identity data, one of the most linguistically diverse and noisy environments globally. SGER achieves 99.02% accuracy and an F1 of 0.994 on a held-out set of 50,000 real-world pairs, outperforming GPT-4o few-shot prompting and single-stage fine-tuning baselines. The system is fully deployed in production at Dream11, the world's largest fantasy sports platform, serving 250M+ users. Our results demonstrate that curriculum-guided training enables robust, high-precision entity resolution in real-world multilingual systems at scale.