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Large Language Model for Table Processing: A Survey

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arXiv 2024年

作者： Lu, Weizheng Zhang, Jing Fan, Ju Fu, Zihao Chen, Yueguo Du, Xiaoyong School of Information Renmin University of China Beijing100872 China Key Laboratory of Data Engineering and Knowledge Engineering Beijing100872 China WPS Office Kingsoft Co. Zhuhai519080 China

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 table-related tasks. Finally, we highlight several challenges, including diverse user input when serving and slow thinking using chain-of-thought. © 2024, CC BY.

关键词： Question answering

Review of graph construction and graph learning in stock price prediction 9th

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Review of graph construction and graph learning in stock pri...

9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Wang, Yunong Qu, Yi Chen, Zhensong Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Management and Engineering Capital University of Economics and Business Beijing100070 China

Precise prediction of stock prices leads to more profits and more effective risk prevention, which is of great significance to both investors and regulators. Recent years, various kinds of information not directly-relevant with stock prices have received more attention, such as texts, images or connections. These external information has the potential of reflecting or influencing fluctuations, and thus, given the utilization of advanced analyzing techniques, the forecasting performance of stock prices could be promoted substantially. For instance, graph neural network models have expanded into many other disciplines including stock price prediction, and exhibited impressive representation learning ability. However, in stock markets, well-defined graphs are rarely seen and how to formulate the graph structures needed remains a challenging problem. Towards this end, this article presents a comprehensive overview of graph construction and graph learning in stock price prediction, by reviewing the existing studies, summarizing its general paradigm, special cases and proposing possible prospects. Our research not only systematically reveals the feasible ways of constructing graphs in financial markets, but also provides insights for further implementations of graph learning models into stock prediction tasks. © 2022 The Authors. Published by Elsevier B.V.

关键词： Financial markets

IKGN: Intention-aware knowledge Graph Network for POI Recommendation

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IKGN: Intention-aware Knowledge Graph Network for POI Recomm...

IEEE International Conference on data Mining (ICDM)

作者： Xiaoyu Zhu Chenyang Bu Bingbing Dong Shengwei Ji Yi He Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology China School of Artificial Intelligence and Big Data Hefei University China Department of Computer Science Old Dominion University USA Research Center for Knowledge Engineering Zhejiang Lab China

Point-of-Interest (POI) recommendation, pivotal for guiding users to their next interested locale, grapples with the persistent challenge of data sparsity. Whereas knowledge graphs (KGs) have emerged as a favored tool to mitigate the issue, existing KG-based methods tend to overlook two crucial elements: the intention steering users’ location choices and the high-order topological structure within the KG. In this paper, we craft an Intention-aware knowledge Graph (IKG) that harmonizes users’ visit histories, movement trajectories, and location categories to model user intentions. Building upon IKG, our novel Intention-aware knowledge Graph Network (IKGN) delves deeper into the POI recommendation by weighing and propagating node embeddings through an attention mechanism, capturing the unique locational intent of each user. A sequential model like GRU is then employed to ensure a comprehensive representation of users’ short- and long-term location preferences. An empirical study on two real-world datasets validates the effectiveness of our proposed IKGN, with it markedly outshining seven benchmark rival models in both Recall and NDCG metrics. The code of IKGN is available at https://***/Jungle123456/IKGN.

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Combining Monte-Carlo Tree Search with Proof-Number Search

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arXiv 2022年

作者： Doe, Elliot Winands, Mark H.M. Soemers, Dennis J.N.J. Browne, Cameron Games Group Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

Proof-Number Search (PNS) and Monte-Carlo Tree Search (MCTS) have been successfully applied for decision making in a range of games. This paper proposes a new approach called PN-MCTS that combines these two tree-search methods by incorporating the concept of proof and disproof numbers into the UCT formula of MCTS. Experimental results demonstrate that PN-MCTS outperforms basic MCTS in several games including Lines of Action, MiniShogi, Knightthrough, and Awari, achieving win rates up to 94.0%. Copyright © 2022, The Authors. All rights reserved.

关键词： Decision making

Semi-supervised Multi-Label Learning with Missing Labels via Correlation Information

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Semi-supervised Multi-Label Learning with Missing Labels via...

International Joint Conference on Neural Networks (IJCNN)

作者： Zexian Xie Peipei Li Jinling Jiang Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Knowledge Engineering Research Center Zhejiang Lab Hangzhou Zhejiang China

In multi-label learning, each instance is associated with a set of labels simultaneously. Most existing studies assume that the set of labels for each instance is complete. However, it is generally difficult to obtain all the relevant labels of each instance, and only a partial or even empty set of relevant labels is available, which is called semi-supervised multi-label learning with missing labels. To tackle this problem, we propose a novel framework that considers label correlations and instance correlations to recover the missing labels and utilizes a large amount of unlabeled data simultaneously to improve the classification performance. Specifically, a new supplementary label matrix is firstly obtained by learning the label correlation. Secondly, considering each class label may be decided by some specific characteristics of its own, a label-specific data representation is hence learned for each class label. Thirdly, instance correlations are utilized not only to recover the missing labels, but also to propagate the supervision information from labeled instances to unlabeled ones. In addition, a united objective function is designed to facilitate the above processing and an accelerated proximal gradient method is adopted to solve the optimization problem. Finally, extensive experimental results conducted on several benchmark datasets demonstrate the effectiveness of the proposed method compared to competing ones.

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Evolutionary Algorithms with Heuristic Gradient-based Repair for Constrained Optimization

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Evolutionary Algorithms with Heuristic Gradient-based Repair...

2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Du, Jiacheng Bu, Chenyang Liu, Yuxin Liu, Fei Luo, Wenjian Hefei University of Technology Ministry of Education Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Anhui Hefei230027 China Harbin Institute of Technology School of Computer Science and Technology Guangdong Shenzhen518000 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Gradient-based repair aims to repair infeasible solutions to feasible ones using the gradient information of the constraints. As an effective constraint handling method, gradientbased repair has received extensive attention and has been applied in various evolutionary algorithms (EAs). Nevertheless, due to the complexity of constraints in practical problems, a single infeasible solution often needs to be repaired multiple times until it becomes a feasible solution or reaches the maximum number of repairs. As far as we know, existing related research on gradient-based repair mainly applies this method directly to EAs, while there is little work in the evolutionary computing community on how to improve gradient-based repair. Currently, the multiple repairs for a single individual are independent. That is, the current repair does not consider the previous repair experience. However, only using gradient information to repair infeasible individuals may result in oscillations in the search process. Therefore, in this paper, we propose a heuristic gradient-based repair method (HGR) which exploits the previous repair information of an individual to alleviate this issue. Experimental results on several benchmarks demonstrate the effectiveness of the proposed method. The source code is available at https://***/DMiC-Lab-HFUT/HGR-SMC2022. © 2022 IEEE.

关键词： Constrained optimization

Research Topic Flows in Co-Authorship Networks

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arXiv 2022年

作者： Schäfermeier, Bastian Hirth, Johannes Hanika, Tom Knowledge and Data Engineering Group University of Kassel Wilhelmshoeher Allee 73 Kassel Germany

In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that Topic Flow Networks are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research *** Codes 68T50 68U35 68V35 01A90 00A15 Copyright © 2022, The Authors. All rights reserved.

关键词： Flow graphs

Mapping Research Trajectories

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arXiv 2022年

作者： Schäfermeier, Bastian Stumme, Gerd Hanika, Tom Knowledge and Data Engineering University of Kassel Wilhelmshoeher Allee 73 Kassel34121 Germany

Steadily growing amounts of information, such as annually published scientific papers, have become so large that they elude an extensive manual analysis. Hence, to maintain an overview, automated methods for the mapping and visualization of knowledge domains are necessary and important, e.g., for scientific decision makers. Of particular interest in this field is the development of research topics of different entities (e.g., scientific authors and venues) over time. However, existing approaches for their analysis are only suitable for single entity types, such as venues, and they often do not capture the research topics or the time dimension in an easily interpretable manner. Hence, we propose a principled approach for mapping research trajectories, which is applicable to all kinds of scientific entities that can be represented by sets of published papers. For this, we transfer ideas and principles from the geographic visualization domain, specifically trajectory maps and interactive geographic maps. Our visualizations depict the research topics of entities over time in a straightforward interpretable manner. They can be navigated by the user intuitively and restricted to specific elements of interest. The maps are derived from a corpus of research publications (i.e., titles and abstracts) through a combination of unsupervised machine learning methods. In a practical demonstrator application, we exemplify the proposed approach on a publication corpus from machine learning. We observe that our trajectory visualizations of 30 top machine learning venues and ∼1000 major authors in this field are well interpretable and are consistent with background knowledge drawn from the entities' publications. Next to producing interactive, interpretable visualizations supporting different kinds of analyses, our computed trajectories are suitable for trajectory mining applications in the *** Codes 68U35, 68U15. Copyright © 2022, The Authors. All rights reserved.

关键词： Mapping

CompressStreamDB: Fine-Grained Adaptive Stream Processing without Decompression

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CompressStreamDB: Fine-Grained Adaptive Stream Processing wi...

International Conference on data engineering

作者： Yu Zhang Feng Zhang Hourun Li Shuhao Zhang Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering (MOE) and School of Information Renmin University of China Information Systems Technology and Design Pillar Singapore University of Technology and Design

Stream processing prevails and SQL query on streams has become one of the most popular application scenarios. For example, in 2021, the global number of active IoT endpoints reaches 12.3 billion. Unfortunately, the increasing scale of data and strict user requests place much pressure on existing stream processing systems, requiring high processing throughput with low latency. To further improve the performance of current stream processing systems, we propose a compression-based stream processing engine, called CompressStreamDB, which enables adaptive fine-grained stream processing directly on compressed streams, without decompression. Particularly, CompressStreamDB involves eight compression methods targeting various data types in streams, and it also provides a cost model for dynamically selecting the appropriate compression methods. By exploring data redundancy among streams, CompressStreamDB not only saves space in data transmission between client and server, but also achieves high throughput with low latency in SQL query on stream processing. Our experimental results show that compared to the state-of-the-art stream processing system on uncompressed streams, CompressStreamDB achieves 3.24× throughput improvement and 66.0% lower latency on average. Besides, CompressStreamDB saves 66.8% space.

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