Query Information
Query information research focuses on improving how information is retrieved and utilized, particularly within complex systems like large language models (LLMs) and databases. Current research emphasizes enhancing query processing through techniques like query rewriting, debiasing, and augmentation using LLMs and various attention mechanisms (e.g., grouped query attention), often incorporating multi-modal data and feedback mechanisms for improved accuracy and efficiency. This work is significant for advancing information retrieval across diverse applications, including question answering, video moment retrieval, and even enhancing the security and reliability of LLMs themselves.
Papers
Perceive, Query & Reason: Enhancing Video QA with Question-Guided Temporal Queries
Roberto Amoroso, Gengyuan Zhang, Rajat Koner, Lorenzo Baraldi, Rita Cucchiara, Volker Tresp
Referencing Where to Focus: Improving VisualGrounding with Referential Query
Yabing Wang, Zhuotao Tian, Qingpei Guo, Zheng Qin, Sanping Zhou, Ming Yang, Le Wang
Queries, Representation & Detection: The Next 100 Model Fingerprinting Schemes
Augustin Godinot, Erwan Le Merrer, Camilla Penzo, François Taïani, Gilles Trédan
Trigger$^3$: Refining Query Correction via Adaptive Model Selector
Kepu Zhang, Zhongxiang Sun, Xiao Zhang, Xiaoxue Zang, Kai Zheng, Yang Song, Jun Xu