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Call for Papers Special Issue of Tsinghua Science and Technology on Data Mining and Knowledge Discovery
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《Tsinghua Science and Technology》 SCIE EI CAS 2013年第2期206-206,共1页
Tsinghua Science and Technology is founded and published since 1996. It is an international academic journal sponsored by Tsinghua University and is published bimonthly. This journal aims at presenting the up-to-date ... Tsinghua Science and Technology is founded and published since 1996. It is an international academic journal sponsored by Tsinghua University and is published bimonthly. This journal aims at presenting the up-to-date scientific achievements in computer science, and other information technology fields. It is indexed by Ei and other abstracting and indexing services. From 2013, the journal commits to the open access at IEEE Xplore Digital Library. 展开更多
关键词 Call for Papers Special Issue of Tsinghua Science and Technology on data mining and knowledge discovery
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Large language model for table processing: a survey
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作者 Weizheng LU Jing ZHANG +3 位作者 Ju FAN Zihao FU Yueguo CHEN Xiaoyong DU 《Frontiers of Computer Science》 2025年第2期71-87,共17页
Tables,typically two-dimensional and structured to store large amounts of data,are essential in daily activities like database queries,spreadsheet manipulations,Web table question answering,and image table information... Tables,typically two-dimensional and structured to store large amounts of data,are essential in daily activities like database queries,spreadsheet manipulations,Web table question answering,and image table information extraction.Automating these table-centric tasks with Large Language Models(LLMs)or Visual Language Models(VLMs)offers significant public benefits,garnering interest from academia and industry.This survey provides a comprehensive overview of table-related tasks,examining both user scenarios and technical aspects.It covers traditional tasks like table question answering as well as emerging fields such as spreadsheet manipulation and table data analysis.We summarize the training techniques for LLMs and VLMs tailored for table processing.Additionally,we discuss prompt engineering,particularly the use of LLM-powered agents,for various tablerelated tasks.Finally,we highlight several challenges,including diverse user input when serving and slow thinking using chainof-thought. 展开更多
关键词 data mining and knowledge discovery table processing large language model
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