Information Retrieval
Information retrieval (IR) focuses on efficiently finding relevant documents or information within large datasets in response to user queries. Current research emphasizes improving retrieval accuracy and efficiency through advancements in semantic understanding, particularly using multimodal data (text, images, tables) and advanced embedding models within retrieval-augmented generation (RAG) frameworks. These improvements are crucial for various applications, including search engines, question answering systems, and knowledge-based applications across diverse domains like healthcare and legal research, ultimately enhancing access to and understanding of information.
Papers
Streamlining Conformal Information Retrieval via Score Refinement
Yotam Intrator, Ori Kelner, Regev Cohen, Roman Goldenberg, Ehud Rivlin, Daniel Freedman
Unified Multimodal Interleaved Document Representation for Retrieval
Jaewoo Lee, Joonho Ko, Jinheon Baek, Soyeong Jeong, Sung Ju Hwang
BayesCNS: A Unified Bayesian Approach to Address Cold Start and Non-Stationarity in Search Systems at Scale
Randy Ardywibowo, Rakesh Sunki, Lucy Kuo, Sankalp Nayak