Passage Retrieval

Passage retrieval aims to efficiently identify relevant text snippets from large corpora in response to a query, a crucial step in many information retrieval tasks. Current research emphasizes improving retrieval accuracy and efficiency through advanced neural network architectures like dense retrievers and transformer-based models, often incorporating techniques such as query rewriting, reranking, and multimodal approaches to handle diverse input types (e.g., speech). These advancements are driving progress in open-domain question answering, conversational AI, and other applications requiring effective information access from large-scale text collections, particularly in low-resource language settings.