A new framework called Structure-Guided Entity Resolution (SGER) has been developed to improve how Large Language Models (LLMs) match names, particularly in complex linguistic situations. SGER uses a two-phase curriculum to first teach the LLM about name structures and then optimize it for entity matching. This approach achieved 99.02% accuracy and an F1 score of 0.994 on Indian identity data, outperforming existing methods like GPT-4o prompting. The SGER system is now in production at Dream11, a platform serving over 250 million users, demonstrating its scalability and effectiveness in real-world multilingual applications. AI
IMPACT Enhances LLM capabilities for precise name matching in multilingual, real-world systems, crucial for KYC and user identity unification.
RANK_REASON The cluster describes a novel research framework and its evaluation in a scientific paper, including benchmark results and a production deployment.
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