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
Knowing Where to Focus: Attention-Guided Alignment for Text-based Person Search
Lei Tan, Weihao Li, Pingyang Dai, Jie Chen, Liujuan Cao, Rongrong Ji
Think&Cite: Improving Attributed Text Generation with Self-Guided Tree Search and Progress Reward Modeling
Junyi Li, Hwee Tou Ng
ClusterTalk: Corpus Exploration Framework using Multi-Dimensional Exploratory Search
Ashish Chouhan, Saifeldin Mandour, Michael Gertz
Isotropy Matters: Soft-ZCA Whitening of Embeddings for Semantic Code Search
Andor Diera, Lukas Galke, Ansgar Scherp
BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving
Teng Wang, Wing-Yin Yu, Zhenqi He, Zehua Liu, Xiongwei Han, Hailei Gong, Han Wu, Wei Shi, Ruifeng She, Fangzhou Zhu, Tao Zhong
CUE-M: Contextual Understanding and Enhanced Search with Multimodal Large Language Model
Dongyoung Go, Taesun Whang, Chanhee Lee, Hwa-Yeon Kim, Sunghoon Park, Seunghwan Ji, Jinho Kim, Dongchan Kim, Young-Bum Kim
A Survey on E-Commerce Learning to Rank
Md. Ahsanul Kabir, Mohammad Al Hasan, Aritra Mandal, Daniel Tunkelang, Zhe Wu