Search Query
Search query optimization is a broad field aiming to improve the efficiency and effectiveness of information retrieval across diverse applications, from game playing and code generation to scientific literature exploration and medical image analysis. Current research focuses on developing novel algorithms, such as adaptive Monte Carlo Tree Search and various transformer-based architectures, to enhance search strategies and reduce computational costs. These advancements have significant implications for various fields, improving the speed and accuracy of tasks ranging from AI decision-making to large-scale data analysis and medical diagnosis.
Papers
Grounding by Trying: LLMs with Reinforcement Learning-Enhanced Retrieval
Sheryl Hsu, Omar Khattab, Chelsea Finn, Archit Sharma
Community search signatures as foundation features for human-centered geospatial modeling
Mimi Sun, Chaitanya Kamath, Mohit Agarwal, Arbaaz Muslim, Hector Yee, David Schottlander, Shailesh Bavadekar, Niv Efron, Shravya Shetty, Gautam Prasad
AmazonQAC: A Large-Scale, Naturalistic Query Autocomplete Dataset
Dante Everaert, Rohit Patki, Tianqi Zheng, Christopher Potts
Improving Pinterest Search Relevance Using Large Language Models
Han Wang, Mukuntha Narayanan Sundararaman, Onur Gungor, Yu Xu, Krishna Kamath, Rakesh Chalasani, Kurchi Subhra Hazra, Jinfeng Rao
Deep Memory Search: A Metaheuristic Approach for Optimizing Heuristic Search
Abdel-Rahman Hedar, Alaa E. Abdel-Hakim, Wael Deabes, Youseef Alotaibi, Kheir Eddine Bouazza
Semantic-guided Search for Efficient Program Repair with Large Language Models
Thanh Le-Cong, Bach Le, Toby Murray