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
APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking
Can Jin, Hongwu Peng, Shiyu Zhao, Zhenting Wang, Wujiang Xu, Ligong Han, Jiahui Zhao, Kai Zhong, Sanguthevar Rajasekaran, Dimitris N. Metaxas
SEC-QA: A Systematic Evaluation Corpus for Financial QA
Viet Dac Lai, Michael Krumdick, Charles Lovering, Varshini Reddy, Craig Schmidt, Chris Tanner
DIRAS: Efficient LLM Annotation of Document Relevance in Retrieval Augmented Generation
Jingwei Ni, Tobias Schimanski, Meihong Lin, Mrinmaya Sachan, Elliott Ash, Markus Leippold
PromptDSI: Prompt-based Rehearsal-free Instance-wise Incremental Learning for Document Retrieval
Tuan-Luc Huynh, Thuy-Trang Vu, Weiqing Wang, Yinwei Wei, Trung Le, Dragan Gasevic, Yuan-Fang Li, Thanh-Toan Do
Debate as Optimization: Adaptive Conformal Prediction and Diverse Retrieval for Event Extraction
Sijia Wang, Lifu Huang