Users who need several queries before finding what they need can benefit from an automatic search assistant that provides feedback on their query modification strategies. We present a method to learn from a search log which types of query modifications have and have not been effective in the past. The method analyses query modifications along two dimensions: a traditional term-based dimension and a semantic dimension, for which queries are enriches with linked data entities. Applying the method to the search logs of two search engines, we identify six opportunities for a query modification assistant to improve search: modification strategies that are commonly used, but that often do not lead to satisfactory results.
FinAI Data Assistant: LLM-based Financial Database Query Processing with the OpenAI Function Calling API
Mirae Asset Global Investments AI Quant Lab, MODULABS Seoul Republic of Korea；Meritz Fire \& Marine Insurance AI Quant Lab, MODULABS Seoul Republic of Korea；Elice AI Quant Lab, MODULABS Seoul Republic of Korea；AI Quant Lab, MODULABS
本研究提出结合LLM与OpenAI Function Calling API的FinAI Data Assistant金融数据库查询方案，通过路由参数化查询提升可靠性与效率，实验验证其在延迟、成本和可靠性上优于text-to-SQL基线。
A Natural Language Query Interface for Searching Personal Information on Smartwatches
Dartmouth College（达特茅斯学院）；Liverpool John Moore University（利物浦约翰摩尔斯大学）；Vienna University of Technology（维也纳工业大学）；University of Vienna（维也纳大学）；University of California, Riverside（加州大学河滨分校）
MedViz: An Agent-based, Visual-guided Research Assistant for Navigating Biomedical Literature
Department of Biomedical Informatics & Data Science, School of Medicine, Yale University（耶鲁大学医学院生物医学信息学与数据科学系）；School of Information & College of Engineering, University of Michigan（密歇根大学信息学院与工程学院）
Developing an AI Assistant for Knowledge Management and Workforce Training in State DOTs
Department of Computer Science, University of Houston（计算机科学系，休斯顿大学）；Department of Civil and Environmental Engineering, University of Houston（土木与环境工程系，休斯顿大学）；Ph.D Department of Civil and Environmental Engineering, University of Houston（土木与环境工程系，休斯顿大学）；Engineering Data Science, University of Houston（工程数据科学，休斯顿大学）
A Data-Centric Approach to Multilingual E-Commerce Product Search: Case Study on Query-Category and Query-Item Relevance
Beijing Jiaotong University（北京交通大学）；Xi’an University of Architecture and Technology（西安建筑科技大学）；Harbin University of Science and Technology（哈尔滨理工大学）；Cangzhou Jiaotong College（沧州交通学院）
Bi-directional Training for Composed Image Retrieval via Text Prompt Learning
Australian National University（澳大利亚国立大学）；Idiap Research Institute（Idiap 研究所）；Australian Institute for Machine Learning, University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）
Session-Aware Query Auto-completion using Extreme Multi-label Ranking
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Google Research（谷歌研究院）；Amazon（亚马逊）；University of California, San Diego（加利福尼亚大学圣迭戈分校）；University of Texas Austin（得克萨斯大学奥斯汀分校）
