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
Harnessing Retrieval-Augmented Generation (RAG) for Uncovering Knowledge Gaps
Joan Figuerola Hurtado
BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics
Jenny Hamer, Eleni Triantafillou, Bart van Merriënboer, Stefan Kahl, Holger Klinck, Tom Denton, Vincent Dumoulin
SM70: A Large Language Model for Medical Devices
Anubhav Bhatti, Surajsinh Parmar, San Lee
On Evaluating the Integration of Reasoning and Action in LLM Agents with Database Question Answering
Linyong Nan, Ellen Zhang, Weijin Zou, Yilun Zhao, Wenfei Zhou, Arman Cohan
On Retrieval Augmentation and the Limitations of Language Model Training
Ting-Rui Chiang, Xinyan Velocity Yu, Joshua Robinson, Ollie Liu, Isabelle Lee, Dani Yogatama