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
Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation
Junde Wu, Jiayuan Zhu, Yunli Qi, Jingkun Chen, Min Xu, Filippo Menolascina, Vicente Grau
Enhancing Healthcare through Large Language Models: A Study on Medical Question Answering
Haoran Yu, Chang Yu, Zihan Wang, Dongxian Zou, Hao Qin