Candidate Retrieval

Candidate retrieval focuses on efficiently identifying a shortlist of potential matches from a large dataset, a crucial preprocessing step in various information retrieval tasks. Current research emphasizes improving retrieval accuracy and diversity through techniques like hierarchical retrieval strategies, embedding-based methods (including those leveraging k-nearest neighbor search and smoothed mixtures), and the integration of language models for both candidate selection and verification. These advancements are driving improvements in applications ranging from question answering and knowledge base completion to ontology alignment and even speech recognition customization, ultimately enhancing the efficiency and accuracy of numerous information processing systems.