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
Enhancing Question Answering Precision with Optimized Vector Retrieval and Instructions
Lixiao Yang, Mengyang Xu, Weimao Ke
CORAG: A Cost-Constrained Retrieval Optimization System for Retrieval-Augmented Generation
Ziting Wang, Haitao Yuan, Wei Dong, Gao Cong, Feifei Li
Towards Multi-Source Retrieval-Augmented Generation via Synergizing Reasoning and Preference-Driven Retrieval
Qingfei Zhao, Ruobing Wang, Xin Wang, Daren Zha, Nan Mu
UniHGKR: Unified Instruction-aware Heterogeneous Knowledge Retrievers
Dehai Min, Zhiyang Xu, Guilin Qi, Lifu Huang, Chenyu You
Multi-Field Adaptive Retrieval
Millicent Li, Tongfei Chen, Benjamin Van Durme, Patrick Xia
AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels
Lei Li, Xiangxu Zhang, Xiao Zhou, Zheng Liu