TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404

MizAR 60 for Mizar 50

Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515

Deep Contextualized Word Representations

Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983

XLNet: Generalized Autoregressive Pretraining for Language Understanding

Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1906.08237 · Cited by 6,687

SQuAD: 100,000+ Questions for Machine Comprehension of Text

Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208

Gaussian Error Linear Units (GELUs)

Hendrycks, D., & Gimpel, K. (2016) · arXiv · doi:10.48550/arxiv.1606.08415 · Cited by 3,537

SpanBERT: Improving Pre-training by Representing and Predicting Spans

Joshi, M., Chen, D., Liu, Y., Weld, D. S., Zettlemoyer, L., & Levy, O. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00300 · Cited by 1,827

Accessing Diverse Web Knowledge with Natural Language Interface

chen, W. (2021) · eScholarship (California Digital Library)

SPARQLing Database Queries from Intermediate Question DecompositionsPreprint

Saparina, I., & Osokin, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.06162

COBE: A Natural Language Code Search Robustness Benchmark

Wehrmann, J., Parraga, O., & Barros, R. C. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892610

TETA: Text-Enhanced Tabular Data Annotation with Multi-task Graph Convolutional Network

Ye, C., Zhi, H., Jiang, S., Zhang, H., Wu, Y., & Dai, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-30675-4_38

Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification

Gupta, J. P., Tay, Y., Kamath, C., Tran, V., Metzler, D., Bavadekar, S., Sun, M., & Gabrilovich, E. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-industry.53

Unveiling the power of transfer learning towards efficient artificial intelligence

Qin, C. (2023) · Digital Repository Service (Northeastern University) · doi:10.17760/d20581917

Text to Data

Li, Y., Radev, D., & Rafiei, D. (2023) · Synthesis lectures on data management · doi:10.1007/978-3-031-45043-3_4

Data-to-Text

Li, Y., Radev, D., & Rafiei, D. (2023) · Synthesis lectures on data management · doi:10.1007/978-3-031-45043-3_6

Weakly Supervised Semantic Parsing with Execution-based Spurious Program Filtering

Lee, K.-I., Kim, S., & Jung, K. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.425

Application of Noise Filter Mechanism for T5-Based Text-to-SQL Generation

Rushdy, M. R. A., & Thayasivam, U. (2023) · Moratuwa Engineering Research Conference (MERCon) · doi:10.1109/mercon60487.2023.10355492 · Cited by 1

KOreaPAS: TAPAS based Korean-Specific Table Question Answering Model

Oh, S., Kho, Y., Lee, Y., & Kang, P. (2023) · Journal of Korean Institute of Industrial Engineers · doi:10.7232/jkiie.2023.49.6.502

Tabular reasoning via two-stage knowledge injection

Shi, Q., Yu, Z., Qi, L., & Liu, T. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-02073-4

Evaluating the Impact of Content Deletion on Tabular Data Similarity and Retrieval Using Contextual Word Embeddings

Berenguer, A., Tomás, D., & Mazón, J.-N. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56060-6_28

Table Orientation Classification Model Based on BERT and TCSMN

Jin, D., Mi, R., & Song, T. (2024) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-57808-3_4

Multi-table Question Answering Method Based on Correlation Evaluation and Precomputed Cube

Wang, B., Wang, C., Zhang, X., Zhao, C., Xiaoling, Y., & Guo, K. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5492-2_30

A Joint Multi-task Learning Model for Web Table-to-Knowledge Graph Matching

Wu, J., & Hou, M. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5492-2_31

Research on natural language query to SQL method with fused table structure

Liu, C., Liao, W., & Xu, Z. C. (2024) · International Conference on Computer Engineering and Application (ICCEA) · doi:10.1109/iccea62105.2024.10603871

Predicting Session-Level Video Streaming Connection Failures by Natural Language Understanding of Historical Tabular Data

Yang, J., Chen, Y., Guo, Y., & Li, C. (2024) · International Conference on Computer and Communication Systems (ICCCS) · doi:10.1109/icccs61882.2024.10602995

Learning Latent Variable for Logical Reasoning in Table-Based Fact Verification

Wu, J., & Hou, M. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651044

On Deep Learning Approaches for Single-Turn Text-to-SQL Parsing: Concepts, Methods, and Future Directions

Ndongala, N. M. (2024) · Texila international journal of academic research · doi:10.21522/tijar.2014.11.03.art009

Development of domain knowledge graph based on semantic annotation of tabular data

Dorodnykh, N. O., & Yurin, A. Y. (2024) · Ontology of Designing · doi:10.18287/2223-9537-2024-14-4-555-568

A Retrieval-Augmented Framework for Tabular Interpretation with Large Language Model

Yan, M., Ren, W., Wang, Y., & Li, J. (2025) · Lecture notes in computer science · doi:10.1007/978-981-97-5779-4_23

Metadata-less Dataset Recommendation Leveraging Dataset Embeddings by Pre-trained Tabular Language Models

Manabe, K., Fujita, Y., Kuwahara, M., & Hayashi, T. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825245

Enhancing Interaction Graph of Data Schema and Syntactic Structure with Pre-trained Language Model for Text-to-SQL

Zhao, W., Zhao, L., Wu, F., Zheng, Z., Jin, H., & Gu, B. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-1024-2_12

Few-Shot Table Question Answering for Bridge Intelligent Management via Domain Adaptation and Retrieval Augmentation

Xia, H., Li, R., Chen, Y., & Wang, W. (2024) · International Conference on Information Science, Computer Technology and Transportation (ISCTT) · doi:10.1109/isctt62319.2024.10875650

TQWERE: Transformer-Based SQL Query Executor

Regev, N., Shabtai, A., & Rokach, L. (2025) · doi:10.5121/csit.2024.150209

Improving SQL Generation with Schema Retrieval and Reaction Mechanism

Shuangtao, Y., Donghai, Z., & Bo, F. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-1809-5_40

SWING: Weakly Supervised Table Question Answering with Self-training via Reinforcement Learning

Cho, S., Kim, H.-L., Lee, J., & Kwon, H. (2025) · IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp64353.2025.00058

Multilingual Table Question Answering for Low-Resource Languages via Selective Question Translation and Cross-Lingual Adaptation

Cho, S., Kim, M., Kim, H.-L., Lee, J., & Kwon, H. (2025) · IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp64353.2025.00057

FAVC: Fine-Grained Attribute-Aware Visual Cross-Modal for Zero-Shot Learning

Chao, X., & Li, H. (2024) · International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) · doi:10.1109/icicml63543.2024.10957786

Retrieval Augmented Generation for Tables in Textual Documents based on Graph Representation Learning

Yang, G., Yuan, Y., Zeng, D., Li, Y., Zhang, J., & Cao, W. (2025) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit63637.2025.10965127 · Cited by 1

Synthetic Tabular Data Detection in the Wild

Kindji, G. C. N., Fromont, É., Rojas-Barahona, L. M., & Urvoy, T. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91398-3_7

Auto-tables: synthesizing multi-step transformations to relationalize tables without using examples

Li, P., He, Y., Yan, C., Wang, Y., & Chaudhuri, S. (2025) · The VLDB Journal · doi:10.1007/s00778-025-00921-z

Visualizations of Natural Texts: A Survey on Text-to-Structured Data Transformations

Rahman, S., Labib, M. A., & Murad, H. (2024) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit64611.2024.11022561

Optimising SQL Generation for Afrikaans Through an English-to-Afrikaans Double Translation Method

Jooste, R., Rananga, S., & Sindane, T. (2025) · IST-Africa Conference (IST-Africa) · doi:10.23919/ist-africa67297.2025.11060527

Enhancing Structural Awareness in Table-To-Text Generation Through Multi-Task Learning

Zheng, Y., Li, M., & Kong, F. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-9994-0_10

Artificial intelligence for information extraction from cement literature

Zaki, M. F. M., Bharadwaj, K., Goyal, R., Jayadeva, J., & Krishnan, N. M. A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-26566-2.00024-6

Адаптация подхода SimCLR к аннотированию столбцов русскоязычных таблиц

Tobola, K. V., & Dorodnykh, N. O. (2025) · International Workshop on Information, Computation, and Control Systems for Distributed Environments (ICCS-DE) · doi:10.47350/iccs-de.2025.34

iTBLS: A Dataset of Interactive Conversations Over Tabular Information

Sundar, A., Richardson, C., Heck, L. P., & Avsian, A. (2025) · Table Representation Learning Workshop (TRL) · doi:10.18653/v1/2025.trl-1.6

TraceTune: Targeted Fine-Tuning of Attention Heads for Text-to-SQL

Zamankhani, S., & Sattler, K.-U. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-02215-8_25

KnowTrans: Boosting Transferability of Data Preparation LLMs via Knowledge Augmentation

Ge, Y., Li, F., Mao, Y., Yang, Y., Ge, C., Chen, Z., Long, J., & Gao, Y. (2025) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65448.2025.00214

LIFTus: An Adaptive Multi-Aspect Column Representation Learning for Table Union Search

Qiu, E., Gao, J., Tu, Y., & Yang, J. (2025) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65448.2025.00165 · Cited by 1

TableCall: Boosting Table Question Answering with Tool-Driven Training-Free LLMs

Xu, C.-B., Chen, Y., Li, X.-H., Yin, F., & Liu, C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04630-7_8

ExpliCIT-QA: Explainable Code-Based Image Table Question Answering

Lagos, M. H., Saez, A., Doval, P. A., Vesteiro, J. A., & Cerezo‐Costas, H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05179-0_26

Vehicle Data Asset Recognition Based on Pattern Matching

Chen, J., Huang, C., Wang, X., Tan, C., Luo, W., & Dong, Z. (2025) · Mitteilungen Klosterneuburg · doi:10.61586/xpvdz

Complex Table Question Answering with Multiple Cells Recall Based on Extended Cell Semantic Matching

Chen, H., & Shen, D. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9100265

Enhancing Text-to-SQL with In-Context Learning: A Multi-Agent Approach Based on CHESS

Miyaji, R. O., Fernandes, R. M., Martins, K. F., Melegati, J., & Corrêa, P. L. P. (2025) · Simpósio Brasileiro de Bancos de Dados (SBBD) · doi:10.5753/sbbd.2025.247172

A Foundation Model pre-trained on Serialized EHR RepresentationsPreprint

Qin, Q., Chou, Z., & Li, S. (2025) · doi:10.36227/techrxiv.176127291.14956389/v1

A Survey of Large Language Models for Tabular Data Imputation: Tuning Paradigms and Challenges

Jha, S., Goyal, V., & Meena, S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-03740-4_19

Financial Statement Automatic Classification and Prediction Model Based on Convolutional Neural Networks

Gu, M., Wang, W., & Deng, Q. (2025) · International Conference on Generative Artificial Intelligence for Business · doi:10.1145/3766918.3766986

An Evaluation of Data Pipelines with Large Language Models

Surbiryala, J., Bal, S., & Chakravorty, A. (2025) · Frontiers of artificial intelligence, ethics and multidisciplinary applications. · doi:10.1007/978-981-96-7945-4_9

The ReQAP System for Question Answering over Personal Information

Christmann, P., & Weikum, G. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761494

2Columns1Row: A Russian Benchmark for Textual and Multimodal Table Understanding and Reasoning

Saburov, V., Vodolazsky, D., Sazanakov, D., & Fenogenova, A. (2025) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2025.findings-emnlp.721

CoAgt: unleashing the reasoning capabilities of large language models on tabular data with a chain of agents

Alrayzah, A., & Alqhtani, S. M. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.3423

SQuARE: Structured Query & Adaptive Retrieval Engine for Tabular Formats

Gondhalekar, C., Patel, U., & Yeh, F.-C. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11400858

VTQA: visual tabular data question answering framework based on multi-agent

Fu, Y., Liu, Y., Zhou, S., Li, S., Zhao, B., Wang, Y., Mao, J., & Zhou, Z. (2025) · Journal of Visualization · doi:10.1007/s12650-025-01103-3

Contrastive Learning-Enhanced Chain-of-Thought Optimization for Complex Table Question Answering

Yin, J., Liu, P., Mao, X., Zou, Z., Zhang, L., Wang, F., Yan, J., Sun, X., & Xu, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3649651

Decoupling Tasking and Language: Modular Low-Rank Adaptation for Effective and Efficient Cross-Lingual Table Understanding

Rao, Z., & Wang, J. (2025) · International Conference on Information Science and Technology · doi:10.1109/icist66592.2025.11306666

Annotating Table Metadata with Knowledge-Enhanced Pre-Trained Language Model

Zhang, Q., Tang, Y., Tian, J., Li, M., & Fang, X. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-4158-4_8

TD-RAG: Time-aware Dynamic-window Retrieval-Augmented Generation for Risk Early Warning

Xu, J., Deng, O., & Jin, Q. (2025) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/cyberscitech68397.2025.00014

Tabular Question Answering System Based on Hybrid Retrieval

Chen, J., Huang, Z., & Zhang, Y. (2025) · International Conference on Digital Economy and Computer Science (DECS) · doi:10.1145/3785706.3785904

Toward real-world Table Agents: capabilities, workflows, and design principles for LLM-based table intelligence

Tian, J., Li, L., Ye, W., Wang, H., Wang, L., Yu, L., Ren, Z., Chen, G., & Zhao, J. (2026) · World Wide Web · doi:10.1007/s11280-025-01399-z · Cited by 3

Context-Aware Data Imputation: Application of Domain-Agnostic Deep Imputation Network

Zamil, M. G. H. A., & AlOmeir, O. (2026) · IEEE Access · doi:10.1109/access.2026.3656379

Fine-Tuning DeepSeek for Efficient and Accurate Natural Language Querying on CSV Data

K, A. B., Thomas, A., Salim, A., Bobus, E., Jullu, E., & Neenu, R. (2025) · International Conference on Responsible, Generative and Explainable AI (ResGenXAI) · doi:10.1109/resgenxai64788.2025.11344035

TRIDENT: Tabular Representation Inference with Dedicated Embeddings for Null Tokens

Rigueira, P. B., Mello, V. F., Evangelista, G. H. G., Grossi, C. S., Machado, G. A. M., Dutenhefner, P. R., Meira, W., & Pappa, G. L. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-15984-7_39

A Framework for Semantic Annotation of Russian-Language Tables Using Deep Learning

Bychkov, I. V., Dorodnykh, N. O., Tobola, K. V., & Yurin, A. Y. (2026) · IEEE Access · doi:10.1109/access.2026.3665201

GenSQL: A Generative AI Framework for Natural Language to SQL Conversion

Das, S., Gadde, S., Reddy, M. A., & Reddy, M. V. V. (2025) · IEEE International Conference on Green Industrial Electronics and Sustainable Technologies (GIEST) · doi:10.1109/giest66547.2025.11387480

Mitigating Understanding Bias: Explicit Meta Enhancement in LLM-based Table Reasoning

Lin, J., Mao, Q., Cheng, M., & Liu, Q. (2026) · IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp68355.2026.00018

Natural-Language Finance Agents: Architectures, Governance, and Human–AI Collaboration for Enterprise-Scale Analytics

Raja, K. A. (2026) · Journal of Information Systems Engineering & Management · doi:10.52783/jisem.v11i2s.14427

A Preoperative Data Sentenceization Method for Postoperative Major Adverse Cardiovascular Event Prediction

Luo, Y., Wang, Y., Peng, X., Zhou, R., Chen, G., Hao, X., & Zhu, T. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11401522

Automatic Prompt Engineering with No Task Cues and No Tuning

Chowdhury, F. M., Mihindukulasooriya, N., D'Souza, N. S., Samulowitz, H., Gupta, N., Hanusiak, T., & Kapitonow, M. (2025) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw69685.2025.00334

MTC-SQL: a memory-enhanced and task-decomposed correction method for text-to-SQL

Zhao, T., Zhang, H., Jiang, Y., Ma, C., & Huang, H. Y. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-025-00995-6

A comprehensive survey on table question answering: Datasets, methods and future directions

Xu, W., Liu, Y., Lu, L., Li, H., & Sun, G. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114459

NL2SQL-OLM: A Novel Framework for Natural Language to SQL with Schema-Aware LLM Prompting and Zero-Shot Query Generation

Ghadiyaram, R., Krishnamoorthy, D., Vanam, L., & Eripilla, J. (2025) · IEEE International Conference on Advances in Data-Driven Analytics and Intelligent Systems (ADACIS) · doi:10.1109/adacis65663.2025.11437141

From Numbers to Narratives: Efficient Language Model-Based Detection for Safety-Critical Minority Classes

Hayat, A., Tridle, H., & Hasan, M. R. (2026) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2026.findings-eacl.258

Efficient Table Retrieval and Understanding with Multimodal Large Language Models

Xu, Z., Fang, H., Han, B., Min, B., Wang, B., Hu, C., & Zhang, S. (2026) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2026.findings-eacl.226 · Cited by 2

WhiteME at U4 Shared Task: Hybrid Retrieval with Table-Structured Clues for Economic Table QA

Tanaka, K., Shirafuji, D., & Saito, T. (2025) · Institutional Repositories DataBase (IRDB) · doi:10.20736/0002002093

Synthesizing Tabular Transformations from Examples using Transformer-based Neural Networks

宇治橋, 善., & Ujibashi, Y. (2022) · SOKENDAI (Graduate University for Advanced Studies)

Table-Aware Row-Level RAG for Classical Chinese Understanding

Liu, Z., & Leong, W. Y. (2026) · Computers · doi:10.3390/computers15040221

A framework for the continuous curation of a knowledge base system

Ahmadi, N. (2021) · theses.fr (ABES) · doi:10.70675/7f54e1cez3f33z4329z8915z355f69842034

Analysis and control of online interactions through neural natural language processing

Laugier, L. (2022) · theses.fr (ABES) · doi:10.70675/4743aa15z209az4addz8f29z95150aa230b8

Employing Transformers and Humans for Textual-Claim Verification

Saeed, M. (2022) · theses.fr (ABES) · doi:10.70675/3e02a3a6z8281z4d84z8f38ze617eaaf969b

Improving Trust in Fact-Checking Systems with Synthetic Training Data and Explanations

Bussotti, J.-F. (2024) · theses.fr (ABES) · doi:10.70675/6f25f951z9b70z4941z86e9zaaa746e7effe

Semantic Annotations for Tabular Data Using Embeddings : Application to Datasets Indexing and Table Augmentation

Liu, J. (2023) · doi:10.70675/fb87e02ezdf51z4394za644z24192af8668d

STAR: Semantic Table Representation with Header-Aware Clustering and Adaptive Weighted Fusion

Hsu, S.-H., Chou, T.-H., Yu, C.-J., & Fan, Y.-C. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792962

ICRD: Self-supervised medical multimodal representation learning with intra-modality contrastive learning and replaced token detection

Zhou, Y., Li, X., Li, B., Zhang, Y., & Zhou, F. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.134953

Federation of heterogeneous models with machine learning-assisted model views

Miranda, J. P. (2025) · theses.fr (ABES) · doi:10.70675/de89abcdzc714z461cz8300z607cc2293274

Retrieval Augmentation Reduces Factual Errors in Knowledge-Intensive Language Model Tasks

Dai, T., Zhang, C., & Zou, J. (2026) · Jisuanji shenghuojia. · doi:10.54097/8jvwpk07

SemTabla: A Human-in-the-Loop Framework for Semantic Enrichment and Validation of Data Tables

Jin, Z., Mi, Y., Zhu, Y., yao, Y., Yu, C., & Xu, K. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791449

Detection of Shock Risk Triggers Using Time-Aware Dynamic-Window RAG with LLMs

Xu, J., Deng, O., & Jin, Q. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-23241-0_17

Agent-Centric Column-Aware Prompt Optimization for Structured Data Tasks with LLMs

Koc, V., & Kamanuru, V. R. (2026) · International Seminar on Intelligent Business and Edge-Computing Research (ISIBER) · doi:10.1109/isiber68248.2026.11470127

A Keyword Query System for Non-Public Database Schemas Based on Semantic-Enhanced Inverted Indexing

Xie, Y., Wang, Y., Lai, H., Xiaoke, X., Li, C., & Ge, E. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461560

Generalizing Numerical Reasoning in Table Data through Operation Sketches and Self-Supervised Learning

Cho, H., Yoo, G., Kim, H., & Lee, J.-Y. (2026) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.788

A Comparative Analysis of an LLM and RAG (Retrrieval Augmented Generation) in a Question and Answer System

Balogun, E. B., Wimmer, H., & Rebman, C. M. (2026) · Journal of Information Systems Applied Research and Analytics · doi:10.62273/rjmj2996

Multi-Granularity Embedding Meets LLM: Improving Table Matching in Scientific Documents

Li, C., Ma, Z., Li, H., Liao, C., Chen, J., & Wang, J. (2026) · International Conference on Electronics Technology and Artificial Intelligence (ETAI) · doi:10.1109/etai68332.2026.11485336

Making SQL Executable with Execution-Guided Text-to-SQL: A Reproducible Study on WikiSQL-Style Queries

Chau, E., & Peters, M. (2025) · Artificial Intelligence and Machine Learning Review · doi:10.69987/aimlr.2025.60301

CONE: Embeddings for Complex Numerical Data Preserving Unit and Variable Semantics

Shrestha, G., Pyayt, A. L., & Gubanov, M. N. (2026) · Proceedings of the ACM on Management of Data · doi:10.1145/3802049

A Survey of Large Language Model-Based Text-to-SQL Methods

Yan, L., Gong, H., Han, P., & Xu, Y. D. (2025) · Asian Conference on Artificial Intelligence Technology (ACAIT) · doi:10.1109/acait67930.2025.11522096

Generating Synthetic Tabular Data Using Large and Small Language Models

Muça, U., Koubeissy, H., & Makhoul, A. (2026) · IEEE Middle East and North Africa Communications Conference (MENACOMM) · doi:10.1109/menacomm69507.2026.11532894

Text Data Integration

Rahman, M. T., Sacharidis, D., Romero, O., & Nadal, S. (2026) · Springer eBooks · doi:10.1007/978-3-032-18765-9_1

GraPPa: Grammar-Augmented Pre-Training for Table Semantic ParsingPreprint

Yu, T., Wu, C.-S., Lin, X., Wang, B., Tan, Y. C., Yang, X., Radev, D., Socher, R., & Xiong, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.13845 · Cited by 163

TaPas: Weakly Supervised Table Parsing via Pre-training

Herzig, J., Nowak, P. K., Müller, T., Piccinno, F., & Eisenschlos, J. M. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.398 · Cited by 721

TABBIE: Pretrained Representations of Tabular DataPreprint

Iida, H., Thai, D., Manjunatha, V., & Iyyer, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.02584 · Cited by 44

Linking-Enhanced Pre-Training for Table Semantic ParsingPreprint

Qin, B., Wang, L., Hui, B., Geng, R., Cao, Z., Yang, M., Sun, J. Q., & Li, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.09486 · Cited by 5

TabEmb: Joint Semantic-Structure Embedding for Table Annotation

Hoseinzade, E., Wang, K., & Raju, A. D. (2026) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2026.acl-long.757 · Cited by 1