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
A Survey of Controllable Learning: Methods and Applications in Information Retrieval
Chenglei Shen, Xiao Zhang, Teng Shi, Changshuo Zhang, Guofu Xie, Jun Xu
Zero-shot Persuasive Chatbots with LLM-Generated Strategies and Information Retrieval
Kazuaki Furumai, Roberto Legaspi, Julio Vizcarra, Yudai Yamazaki, Yasutaka Nishimura, Sina J. Semnani, Kazushi Ikeda, Weiyan Shi, Monica S. Lam
\'Evaluation des capacit\'es de r\'eponse de larges mod\`eles de langage (LLM) pour des questions d'historiens
Mathieu Chartier, Nabil Dakkoune, Guillaume Bourgeois, Stéphane Jean
Pistis-RAG: Enhancing Retrieval-Augmented Generation with Human Feedback
Yu Bai, Yukai Miao, Li Chen, Dawei Wang, Dan Li, Yanyu Ren, Hongtao Xie, Ce Yang, Xuhui Cai