Structure-Guided Entity Resolution: Fine-Tuning LLMs for Robust Name Matching in Complex Linguistic Contexts
arXiv:2605.23597v1 Announce Type: cross 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 set
The increasing sophistication of LLMs and the growing demand for robust identity verification in complex global data environments are converging to address long-standing challenges in entity resolution.
Improved entity resolution through advanced AI can significantly enhance fraud detection, regulatory compliance (especially KYC), and data quality across numerous industries.
Traditional rule-based or statistical entity resolution systems will be augmented or replaced by more flexible and accurate LLM-driven approaches, particularly in handling linguistic ambiguities.
- · Financial Institutions
- · Identity Verification Services
- · Data Analytics Companies
- · Regulatory Bodies
- · Fraudsters
- · Legacy Data Management Systems
More accurate and efficient customer onboarding and compliance processes.
Reduced operational costs and risk exposure for organizations dealing with large, international datasets.
Potential for new global identity standards or interoperability frameworks built on advanced entity resolution capabilities.
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Read at arXiv cs.LG