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Large language model for table processing: a survey

Frontiers of Computer Science 2025年第2期19卷 71-87页

作者： Weizheng LU Jing ZHANG Ju FAN Zihao FU Yueguo CHEN Xiaoyong DU School of Information Renmin University of ChinaBeijing 100872China Key Laboratory of Data Engineering and Knowledge Engineering Beijing 100872China WPS Office Kingsoft Co.Zhuhai 519080China

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 *** these table-centric tasks with Large Language Models(LLMs)or Visual Language Models(VLMs)offers significant public benefits,garnering interest from academia and *** survey provides a comprehensive overview of table-related tasks,examining both user scenarios and technical *** covers traditional tasks like table question answering as well as emerging fields such as spreadsheet manipulation and table data *** summarize the training techniques for LLMs and VLMs tailored for table ***,we discuss prompt engineering,particularly the use of LLM-powered agents,for various tablerelated ***,we highlight several challenges,including diverse user input when serving and slow thinking using chainof-thought.

关键词： data mining and knowledge discovery table processing large language model

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LIOF: Make the Learned Index Learn Faster With Higher Accuracy

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IEEE Transactions on knowledge and data engineering 2025年第6期37卷 3499-3513页

作者： Ji, Tao Zhong, Kai Sun, Luming Li, Yiyan Li, Cuiping Chen, Hong Ministry of Education Key Laboratory of Data Engineering and Knowledge Engineering Beijing100872 China Renmin University of China School of Information Beijing100872 China Yunxi Technology Company Ltd. Shanghai200120 China

Learned indexes, emerging as a promising alternative to traditional indexes like B+Tree, utilize machine learning models to enhance query performance and reduce memory usage. However, the widespread adoption of learned indexes is limited by their expensive training cost and the need for high accuracy of internal models. Although some studies attempt to optimize the building process of these learned indexes, existing methods are restrictive in scope and applicability. They are usually tailored to specific index types and heavily rely on pre-trained model knowledge, making deployment a challenging task. In this work, we introduce the Learned Index Optimization Framework (LIOF), a general and easily integrated solution aimed at expediting the training process and improving the accuracy of index model for one-dimensional and multi-dimensional learned indexes. The optimization of LIOF for the learned indexes is intuitive, directly providing optimized parameters for index models based on the distribution of node data. By leveraging the correlation between key distribution and node model parameters, LIOF significantly reduces the training epochs required for each node model. Initially, we introduce an optimization strategy inspired by optimization-based meta-learning to train the LIOF to generate optimized initial parameters for index node models. Subsequently, we present a data-driven encoder and a parameter-centric decoder network, which adaptively translate key distribution into a latent variable representation and decode it into optimized node model initialization. Additionally, to further utilize characteristics of key distribution, we propose a monotonic regularizer and focal loss, guiding LIOF training towards efficiency and precision. Through extensive experimentation on real-world and synthetic datasets, we demonstrate that LIOF provides substantial enhancements in both training efficiency and the predictive accuracy for learned indexes. © 1989-2012 IEEE.

关键词： Decoding

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Skeleton-Based Action Recognition Using Graph Convolutional Network with Pose Correction and Channel Topology Refinement

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Computers, Materials & Continua 2025年第4期83卷 701-718页

作者： Yuxin Gao Xiaodong Duan Qiguo Dai School of Engineering Guangzhou College of Technology and BusinessFoshan528138China School of Computer Science and Engineering Dalian Minzu UniversityDalian116000China SEAC key Laboratory of Big Data Applied Technology Dalian Minzu UniversityDalian116000China

Graph convolutional network(GCN)as an essential tool in human action recognition tasks have achieved excellent performance in previous ***,most current skeleton-based action recognition using GCN methods use a shared topology,which cannot flexibly adapt to the diverse correlations between joints under different motion *** video-shooting angle or the occlusion of the body parts may bring about errors when extracting the human pose coordinates with estimation *** this work,we propose a novel graph convolutional learning framework,called PCCTR-GCN,which integrates pose correction and channel topology refinement for skeleton-based human action ***,a pose correction module(PCM)is introduced,which corrects the pose coordinates of the input network to reduce the error in pose feature ***,channel topology refinement graph convolution(CTR-GC)is employed,which can dynamically learn the topology features and aggregate joint features in different channel dimensions so as to enhance the performance of graph convolution networks in feature ***,considering that the joint stream and bone stream of skeleton data and their dynamic information are also important for distinguishing different actions,we employ a multi-stream data fusion approach to improve the network’s recognition *** evaluate the model using top-1 and top-5 classification *** the benchmark datasets iMiGUE and Kinetics,the top-1 classification accuracy reaches 55.08%and 36.5%,respectively,while the top-5 classification accuracy reaches 89.98%and 59.2%,*** the NTU dataset,for the two benchmark RGB+Dsettings(X-Sub and X-View),the classification accuracy achieves 89.7%and 95.4%,respectively.

关键词： Pose correction multi-stream fusion GCN action recognition

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Gupacker: Generalized Unpacking Framework for Android Malware

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4338-4352页

作者： Zheng, Tao Hou, Qiyu Chen, Xingshu Ren, Hao Li, Meng Li, Hongwei Shen, Changxiang Sichuan University School of Cyber Science and Engineering Chengdu610065 China Sichuan University School of Cyber Science and Engineering Cyber Science Research Institute Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province School of Computer Science and Information Engineering Hefei230002 China University of Padua Department of Mathematics HIT Center Padua35131 Italy University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Sichuan University Cyber Science Research Institute Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China

Android malware authors often use packers to evade analysis. Although many unpacking tools have been proposed, they face two significant challenges: 1) They are easily impeded by anti-analysis techniques employed by packers, preventing efficient collection of hidden Dex data. 2) They are typically designed to unpack a specific packer and cannot handle malware packed with mixed packers. Consequently, many packed malware samples evade detection. To bridge this gap, we propose Gupacker , a novel generalized unpacking framework. Gupacker offers a generic solution for first-generation holistic packer by customizing the Android system source code. It identifies the type of packer and selects an appropriate unpacking function, constructs a deeper active call chain to achieve generic unpacking of second-generation function extraction packers, and uses JNI function and instruction monitoring to handle third-generation virtual obfuscation packer. On this basis, we counteract a diverse array of anti-analysis techniques. We conduct extensive experiments on 5K packed Android malware samples, comparing Gupacker with 2 commercial and 4 state-of-the-art academic unpacking tools. The results demonstrate that Gupacker significantly improves the efficiency of Android malware unpacking with acceptable system overhead. We analyze real packed applications based on Gupacker and found several are second-packed by attackers, including WPS for Android, with tens of millions of users. We receive and responsibly report 13 0day vulnerabilities and also assist in the remediation of all vulnerabilities. © 2005-2012 IEEE.

关键词： Android malware

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Federated Incremental Named Entity Recognition 31

Federated Incremental Named Entity Recognition

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Liu, Zesheng Zhu, Qiannan Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Engineering Research Center of Database and Business Intelligence MOE China School of Artificial Intelligence Beijing Normal University Beijing China Engineering Research Center of Intelligent Technology and Educational Application MOE China

ISBN: (纸本)9798891761964

Federated learning-based Named Entity Recognition (FNER) has attracted widespread attention through decentralized training on local clients. However, most FNER models assume that entity types are pre-fixed, so in practical applications, local clients constantly receive new entity types without enough storage to access old entity types, resulting in severe forgetting on previously learned knowledge. In addition, new clients collecting only new entity types may join the global training of FNER irregularly, further exacerbating catastrophic forgetting. To overcome the above challenges, we propose a Forgetting-Subdued Learning (FSL) model which solves the forgetting problem on old entity types from both intra-client and inter-client two aspects. Specifically, for intra-client aspect, we propose a prototype-guided adaptive pseudo labeling and a prototypical relation distillation loss to surmount catastrophic forgetting of old entity types with semantic shift. Furthermore, for inter-client aspect, we propose a task transfer detector. It can identify the arrival of new entity types that are protected by privacy and store the latest old global model for relation distillation. Qualitative experiments have shown that our model has made significant improvements compared to several baseline methods. © 2025 Association for Computational Linguistics.

关键词： Federated learning

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An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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Histological Image Diagnosis of Breast Cancer Based on Multi-Attention Convolution Neural Network

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Journal of Shanghai Jiaotong university(Science) 2025年第1期30卷 91-106页

作者： XU Wangwang XU Liangfeng LIU Ninghui LU Na Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Ministry of EducationHefei230601China School of Computer and Information Hefei University of TechnologyHefei230601China First Affiliated Hospital of Anhui Medical University Hefei230022China

Breast cancer is a serious and high morbidity disease in women,and it is the main cause of cancer death in ***,getting tested and diagnosed early can reduce the risk of *** present,there are clinical examinations,imaging screening and biopsies,among which histopathological examination is the gold ***,the process is complicated and time-consuming,and misdiagnosis may *** paper puts forward a classification framework based on deep learning,introducing multi-attention mechanism,selecting kernel convolution instead of ordinary convolution,and using different weights and combinations to pay attention to the accuracy index and growth rate of the *** addition,we also compared the learning rate *** function can fine-tune the learning rate to achieve good performance,using label softening to reduce the loss error caused by model error recognition in the label,and assigning different category weights in the loss function to balance the positive and negative *** used the BreakHis data set to automatically classify histological images into benign and malignant,four categories and eight *** results showed that the accuracy of binary classifications ranged from 98.23%to 98.83%,and that of multiple classifications ranged from 97.89%to 98.11%.

关键词： breast cancer deep learning attentional mechanism classification diagnosis

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Enhancing Human Pose Estimation in the Internet of Things via Diffusion Generative Models

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IEEE Internet of Things Journal 2025年第10期12卷 13556-13567页

作者： Wu, Sifan Zhang, Hongzhe Liu, Zhenguang Chen, Haipeng Jiao, Yingying Jilin University College of Computer Science and Technology the Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun130012 China School of discipline inspection and supervision China University of Political Science and Law Beijing100088 China Zhejiang University State Key Laboratory of Blockchain and Data Security Hangzhou310027 China Institute of Blockchain and Data Security Hangzhou310059 China Zhejiang University of Technology College of Computer Science and Technology Hangzhou310014 China

With the ongoing development of public video surveillance technology, accurate human pose estimation is becoming increasingly important in urban administration and law enforcement. However, existing methods rely on large-scale dense annotations, which are labor-intensive and time-consuming. To tackle this, we propose SparsePose which leverages training videos with sparse annotations (labeled every k frames) to learn to propagate temporal poses that help to estimate the poses in unlabeled frames. Technically, we engage in a novel dual-branch architecture that combines 1) pose forecasting of the consecutive neighboring frames with 2) visual clues of the current frame and the nearest labeled frames. We theoretically derive the intra-branch and inter-branch mutual information loss to supervise that maximized pose-relevant features are extracted from the current frame and different branches complement each other to approach precise pose estimation. Additionally, we propose a diffusion generative enhancement, which improves the robustness of the model to challenging scenes from the perspective of diversity. Empirical results show that our method significantly outperforms the state-of-the-art methods in sparsely labeled pose estimation on three benchmark datasets. © 2025 IEEE.

关键词： Generative adversarial networks

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A Query Optimization Method Utilizing Large Language Models

arXiv

引用

arXiv 2025年

作者： Yao, Zhiming Li, Haoyang Zhang, Jing Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Engineering Research Center of Database and Business Intelligence MOE China

Query optimization is a critical task in database systems, focused on determining the most efficient way to execute a query from an enormous set of possible strategies. Traditional approaches rely on heuristic search methods and cost predictions, but these often struggle with the complexity of the search space and inaccuracies in performance estimation, leading to suboptimal plan choices. This paper presents LLMOpt, a novel framework that leverages Large Language Models (LLMs) to address these challenges through two innovative components: (1) LLM for Plan Candidate Generation (LLMOpt(G)), which eliminates heuristic search by utilizing the reasoning abilities of LLMs to directly generate high-quality query plans, and (2) LLM for Plan Candidate Selection (LLMOpt(S)), a list-wise cost model that compares candidates globally to enhance selection accuracy. To adapt LLMs for query optimization, we propose fine-tuning pre-trained models using optimization data collected offline. Experimental results on the JOB, JOB-EXT, and Stack benchmarks show that LLMOpt(G) and LLMOpt(S) outperform state-of-the-art methods, including PostgreSQL, BAO, and HybridQO. Notably, LLMOpt(S) achieves the best practical performance, striking a balance between plan quality and inference efficiency. Copyright © 2025, The Authors. All rights reserved.

关键词： Structured Query Language

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CoT-based Synthesizer: Enhancing LLM Performance through Answer Synthesis

arXiv

引用

arXiv 2025年

作者： Zhang, Bohan Zhang, Xiaokang Zhang, Jing Yu, Jifan Luo, Sijia Tang, Jie School of Information Renmin University of China China Tsinghua University China Key Laboratory of Data Engineering and Knowledge Engineering Beijing China

Current inference scaling methods, such as Self-consistency and Best-of-N, have proven effective in improving the accuracy of LLMs on complex reasoning tasks. However, these methods rely heavily on the quality of candidate responses and are unable to produce correct answers when all candidates are incorrect. In this paper, we propose a novel inference scaling strategy, CoT-based Synthesizer, which leverages CoT reasoning to synthesize superior answers by analyzing complementary information from multiple candidate responses, even when all candidate responses are flawed. To enable a lightweight and cost-effective implementation, we introduce an automated data generation pipeline that creates diverse training data. This allows smaller LLMs trained on this data to improve the inference accuracy of larger models, including API-based LLMs. Experimental results across four benchmark datasets with seven policy models demonstrate that our method significantly enhances performance, with gains of 11.8% for Llama3-8B and 10.3% for GPT-4o on the MATH dataset. The corresponding training data and code are publicly available on the repository. Copyright © 2025, The Authors. All rights reserved.

关键词： data accuracy

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