Large language model-assisted entity name resolution

Abstract

Systems and methods for resolving entity names combine hybrid search techniques with large language models to improve accuracy in matching business entities. The system includes an index builder that generates and stores both sparse and dense vector embeddings representing entity information. A query component performs hybrid searches using both keyword-based and semantic methods to retrieve entity candidates from the stored embeddings. When high-confidence matches are not found, a virtual entity generator leverages large language models to generate additional entity candidates based on the input data. The system employs a re-ranker to order retrieved candidates based on their relevance to the input query. This approach enables accurate entity resolution even with noisy or incomplete input data by combining traditional search methods with advanced language model capabilities.