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

WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections

Chen, M., Wiseman, S., & Gimpel, K. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.17 · Cited by 30

Knowledge graphs 2021

Weikum, G. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3476311.3476393 · Cited by 25

Model Breadcrumbs: Scaling Multi-task Model Merging with Sparse Masks

Davari, M., & Belilovsky, E. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73226-3_16 · Cited by 60

Pre-training language model incorporating domain-specific heterogeneous knowledge into a unified representation

Zhu, H., Peng, H. M., Lyu, Z., Hou, L., Li, J., & Xiao, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119369 · Cited by 27

Tele-Knowledge Pre-training for Fault Analysis

Chen, Z., Wen, Z., Huang, Y., Chen, M., Geng, Y., Yu, H., Bi, Z., Zhang, Y., Yao, Z., Song, W., et al. (2023) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde55515.2023.00265 · Cited by 25

Ember

Suri, S., Ilyas, I. F., Ré, C., & Rekatsinas, T. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3494124.3494149 · Cited by 25

Unsupervised Matching of Data and Text

Ahmadi, N., Sand, H., & Papotti, P. (2022) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde53745.2022.00084 · Cited by 25

On Grounded Planning for Embodied Tasks with Language Models

Lin, B. Y., Huang, C., Liu, Q., Gu, W., Sommerer, S., & Ren, X. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i11.26549 · Cited by 31

Boosting Transformers and Language Models for Clinical Prediction in Immunotherapy

Chen, Z., Balan, M. M., & Brown, K. M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-industry.32 · Cited by 40

An Empirical Study of Model Errors and User Error Discovery and Repair Strategies in Natural Language Database Queries

Ning, Z., Zhang, Z., Sun, T., Tian, Y., Zhang, T., & Li, T. J.-J. (2023) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3581641.3584067 · Cited by 23

ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples

Zhao, Y., Nan, L., Qi, Z., Zhang, R., & Radev, D. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.615 · Cited by 31

Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification

Shi, Q., Zhang, Y., Yin, Q., & Liu, T. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.16 · Cited by 21

DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles

Gupta, T., Zaki, M. F. M., Khatsuriya, D., Hira, K., Krishnan, N. M. A., & Mausam, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.753 · Cited by 26

Solo: Data Discovery Using Natural Language Questions Via A Self-Supervised Approach

Wang, Q., & Fernandez, R. C. (2023) · Proceedings of the ACM on Management of Data · doi:10.1145/3626756 · Cited by 33

Speech-to-SQL: toward speech-driven SQL query generation from natural language question

Song, Y., Wong, R. C.-W., & Zhao, X. (2024) · The VLDB Journal · doi:10.1007/s00778-024-00837-0 · Cited by 23

Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning

Li, M., Feng, F., Zhang, H., He, X., Zhu, F., & Chua, T. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.5 · Cited by 21

Contrastive Mixup: Self- and Semi-Supervised learning for Tabular DomainPreprint

Darabi, S., Fazeli, S., Pazoki, A., Sankararaman, S., & Sarrafzadeh, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.12296 · Cited by 37

MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement

Wang, Z., Gao, C., Xiao, C., & Sun, J. (2024) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2024/670 · Cited by 20

Structure-Aware Pre-Training for Table-to-Text Generation

Xing, X., & Wan, X. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.200 · Cited by 20

CLTR: An End-to-End, Transformer-Based System for Cell-Level Table Retrieval and Table Question Answering

Pan, F., Canim, M., Glass, M., Gliozzo, A., & Fox, P. (2021) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.18653/v1/2021.acl-demo.24 · Cited by 19

RECA: Related Tables Enhanced Column Semantic Type Annotation Framework

Sun, Y., Xin, H., & Chen, L. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3583140.3583149 · Cited by 19

University Student Dropout Prediction Using Pretrained Language Models

Won, H.-S., Kim, M., Kim, D., Kim, H., & Kim, K.-M. (2023) · Applied Sciences · doi:10.3390/app13127073 · Cited by 19

Applications and Challenges for Large Language Models: From Data Management Perspective

Zhang, M., Ji, Z., Luo, Z., Wu, Y., & Chai, C. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00441 · Cited by 21

TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data

Zhou, F., Hu, M., Dong, H., Cheng, Z., Cheng, F., Shi, H., & Zhang, D. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.145 · Cited by 19

Auto-Tables: Synthesizing Multi-Step Transformations to Relationalize Tables without Using Examples

Li, P., He, Y., Yan, C., Wang, Y., & Chaudhuri, S. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3611479.3611534 · Cited by 25

SpreadsheetCoder: Formula Prediction from Semi-structured ContextPreprint

Chen, X., Maniatis, P., Singh, R., Sutton, C. A., Dai, H., Lin, M., & Zhou, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.15339 · Cited by 21

Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data

Akhtar, M., Shankarampeta, A., Gupta, V., Patil, A., Cocarascu, O., & Simperl, E. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.1028 · Cited by 17

Towards Cross-Table Masked Pretraining for Web Data Mining

Ye, C., Lu, G., Wang, H., Li, L., Wu, S., Chen, G., & Zhao, J. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645707 · Cited by 32

Unseen Entity Handling in Complex Question Answering over Knowledge Base via Language Generation

Huang, X., Kim, J., & Zou, B. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.50 · Cited by 19

Rethinking data-driven networking with foundation models

Le, F., Srivatsa, M., Ganti, R., & Sekar, V. (2022) · ACM Workshop on Hot Topics in Networks (HotNets) · doi:10.1145/3563766.3564109 · Cited by 21

Table understanding: Problem overview

Shigarov, A. (2022) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1482 · Cited by 17

Joint Verification and Reranking for Open Fact Checking Over Tables

Schlichtkrull, M., Karpukhin, V., Oguz, B., Lewis, M., Yih, W.-T., & Riedel, S. (2021) · doi:10.18653/v1/2021.acl-long.529 · Cited by 20

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

Multi-modal Retrieval of Tables and Texts Using Tri-encoder Models

Kostić, B., Risch, J., & Möller, T. (2021) · Workshop on Machine Reading for Question Answering (MRQA) · doi:10.18653/v1/2021.mrqa-1.8 · Cited by 20

Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach

Chen, M., Lu, X., Xu, T., Li, Y., Jingbo, Z., Dou, D., & Xiong, H. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.562 · Cited by 18

Automatic Table Union Search with Tabular Representation Learning

Hu, X., Wang, S., Qin, X., Lei, C., Shen, Z., Faloutsos, C., Katsifodimos, A., Karypis, G., Wen, L., & Yu, P. S. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.233 · Cited by 22

OPTIMIZING SQL DATABASES FORBIG DATA WORKLOADS: TECHNIQUES AND BEST PRACTICES

Uzzaman, A., Jim, M. M. I., Nishat, N., & Nahar, J. (2024) · Academic journal on business administration, innovation & sustainability. · doi:10.69593/ajbais.v4i3.78 · Cited by 16

Table-Critic: A Multi-Agent Framework for Collaborative Criticism and Refinement in Table Reasoning

Yu, P. S., Chen, G., & Wang, J. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.853 · Cited by 16

DoT: An efficient Double Transformer for NLP tasks with tables

Krichene, S., Müller, T., & Eisenschlos, J. M. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.289 · Cited by 17

Pneuma : Leveraging LLMs for Tabular Data Representation and Retrieval in an End-to-End System

Balaka, M. I. L., Alexander, D., Wang, Q., Gong, Y., Krisnadhi, A. A., & Fernandez, R. C. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3725337 · Cited by 30

Is My Model Using the Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning

Gupta, V., Bhat, R. A., Ghosal, A., Shrivastava, M., Singh, M., & Srikumar, V. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00482 · Cited by 16

Exploring the Landscape of Natural Language Processing Research

Schopf, T., Arabi, K., & Matthes, F. (2023) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-092-2_111 · Cited by 21

MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering

Pal, V., Yates, A., Kanoulas, E., & de Rijke, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.348 · Cited by 15

Generating Realistic Tabular Data with Large Language Models

Nguyen, D., Gupta, S., Do, K., Nguyen, T., & Venkatesh, S. (2024) · Proceedings · doi:10.1109/icdm59182.2024.00040 · Cited by 25

Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalization

Oren, I., Herzig, J., & Berant, J. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.843 · Cited by 18

Transformers for tabular data representation

Badaro, G., & Papotti, P. (2022) · Proceedings of the VLDB Endowment · doi:10.14778/3554821.3554890 · Cited by 15

FiE: Building a Global Probability Space by Leveraging Early Fusion in Encoder for Open-Domain Question Answering

Kedia, A., Zaidi, M. A., & Lee, H. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.285 · Cited by 14

XiYan-SQL: A Novel Multi-Generator Framework for Text-to-SQL

Liu, Y., Zhu, Y., Gao, Y., Luo, Z., Li, X., Shi, X., Hong, Y., Gao, J., Li, Y., Ding, B., et al. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3657851 · Cited by 48

LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation

He, X., Ban, Y., Zou, J., Wei, T., Cook, C. B., & He, J. (2025) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2025.findings-acl.361 · Cited by 25

A Graph Representation of Semi-structured Data for Web Question Answering

Zhang, X., Shou, L., Pei, J., Gong, M., Wen, L., & Jiang, D. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.5 · Cited by 17

Table Retrieval May Not Necessitate Table-specific Model Design

Wang, Z., Jiang, Z., Nyberg, E., & Neubig, G. (2022) · doi:10.18653/v1/2022.suki-1.5 · Cited by 17

Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion

Zhao, C., Su, Y., Pauls, A., & Platanios, E. A. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.381 · Cited by 16

CASSED: Context-based Approach for Structured Sensitive Data Detection

Kužina, V., Petric, A.-M., Barišić, M., & Jović, A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119924 · Cited by 16

Enhanced Chart Understanding via Visual Language Pre-training on Plot Table Pairs

Zhou, M., Fung, Y. R., Chen, L., Thomas, C., Ji, H., & Chang, S. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.85 · Cited by 15

Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA

Huang, J., Zhong, W., Liu, Q., Gong, M., Jiang, D., & Duan, N. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.303 · Cited by 19

Cornet: Learning Table Formatting Rules By Example

Singh, M. P., Cambronero, J., Gulwani, S., Le, V., Negreanu, C., Raza, M., & Verbruggen, G. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3603581.3603600 · Cited by 14

Neural Re-rankers for Evidence Retrieval in the FEVEROUS Task

Saeed, M., Alfarano, G., Nguyen, K., Pham, D., Raphaël Troncy, A., & Papotti, P. (2021) · Fact Extraction and VERification Workshop (FEVER) · doi:10.18653/v1/2021.fever-1.12 · Cited by 14

Low-Resource Task-Oriented Semantic Parsing via Intrinsic ModelingPreprint

Desai, S., Shrivastava, A., Zotov, A., & Aly, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.07224 · Cited by 15

MATE: Multi-view Attention for Table Transformer EfficiencyPreprint

Eisenschlos, J. M., Gor, M., Müller, T., & Cohen, W. W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.04312 · Cited by 14

Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge

Dou, L., Gao, Y., Liu, X., Pan, M., Wang, D., Che, W., Zhan, D., Kan, M., & Lou, J. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.350 · Cited by 13

DTT: An Example-Driven Tabular Transformer for Joinability by Leveraging Large Language Models

Nobari, A. D., & Rafiei, D. (2024) · Proceedings of the ACM on Management of Data · doi:10.1145/3639279 · Cited by 16

Benchmarking TabLM: Evaluating the Performance of Language Models Against Traditional Machine Learning in Structured Data Tasks

Shakir, W. A. (2024) · International Conference on Emerging Technologies for Dependable Internet of Things (ICETI) · doi:10.1109/iceti63946.2024.10777155 · Cited by 14

Is Long Context All You Need? Leveraging LLM's Extended Context for NL2SQL

Chung, Y., Kakkar, G. T., Gan, Y., Milne, B., & Özcan, F. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3742728.3742761 · Cited by 37

Deep learning meets risk-based auditing: A holistic framework for leveraging foundation and task-specific models in audit procedures

Föhr, T. L., Schreyer, M., Moffitt, K. C., & Marten, K.-U. (2026) · International Journal of Accounting Information Systems · doi:10.1016/j.accinf.2025.100758 · Cited by 14

DBTagger

Usta, A., Karakayali, A., & Ulusoy, Ö. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3446095.3446103 · Cited by 16

Parameter-Efficient Abstractive Question Answering over Tables or Text

Pal, V., Kanoulas, E., & de Rijke, M. (2022) · DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc) · doi:10.18653/v1/2022.dialdoc-1.5 · Cited by 14

Dual-Channel Evidence Fusion for Fact Verification over Texts and Tables

Hu, N., Wu, Z., Lai, Y., Liu, X., & Feng, Y. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.384 · Cited by 13

Data Ambiguity Profiling for the Generation of Training Examples

Veltri, E., Badaro, G., Saeed, M., & Papotti, P. (2023) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde55515.2023.00041 · Cited by 13

RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations

Zhao, Y., Zhao, C., Nan, L., Qi, Z., Zhang, W., Tang, X., Mi, B., & Radev, D. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.334 · Cited by 35

TWT: Table with Written Text for Controlled Data-to-Text Generation

Li, T., Fang, L., Lou, J., & Li, Z. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.107 · Cited by 12

SGAMF: Sparse Gated Attention-Based Multimodal Fusion Method for Fake News Detection

Du, P., Gao, Y., Li, L., & Li, X. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2024.3414341 · Cited by 12

Reliable Text-to-SQL with Adaptive Abstention

Chen, K., Chen, Y., Koudas, N., & Yu, X. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3709719 · Cited by 12

Attend, Memorize and Generate: Towards Faithful Table-to-Text Generation in Few Shots

Zhao, W., Ye, L., Wan, Y., & Yu, P. S. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.347 · Cited by 12

ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select

Zhuang, Y., Li, Y., Zhang, J., Yu, Y., Mou, Y., Chen, X., Song, L., & Zhang, C. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.46 · Cited by 12

Leveraging Large Language Models for Sensor Data Retrieval

Berenguer, A., Morejón, A., Tomás, D., & Mazón, J.-N. (2024) · Applied Sciences · doi:10.3390/app14062506 · Cited by 12

Unveiling Challenges for LLMs in Enterprise Data Engineering

Bodensohn, J.-M., Brackmann, U., Vogel, L., Sanghi, A., & Binnig, C. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3773749.3773758 · Cited by 11

Joint Learning of Representations for Web-tables, Entities and Types using Graph Convolutional Network

Pramanick, A., & Bhattacharya, I. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.102 · Cited by 11

SPARQLing Database Queries from Intermediate Question Decompositions

Saparina, I., & Osokin, A. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.708 · Cited by 12

From Tables to Knowledge

Pujara, J., Szekely, P. A., Sun, H., & Chen, M. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3470809 · Cited by 11

GP: Context-free Grammar Pre-training for Text-to-SQL ParsersPreprint

Zhao, L., Cao, H., & Zhao, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.09901 · Cited by 11

GPT-Based Text-to-SQL for Spatial Databases

Wang, H., Guo, L., Liang, Y., Liu, L., & Huang, J. (2025) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi14080288 · Cited by 13

Insights into Natural Language Database Query Errors: from Attention Misalignment to User Handling Strategies

Ning, Z., Tian, Y., Zhang, Z., Zhang, T., & Li, T. J.-J. (2024) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3650114 · Cited by 17

AID-SQL: Adaptive In-Context Learning of Text-to-SQL with Difficulty-Aware Instruction and Retrieval-Augmented Generation

Li, X., Cai, Q., Shu, Y., Guo, C., & Yang, B. (2025) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65448.2025.00294 · Cited by 12

A Survey on Tabular Data: From Tree-based Methods to Tabular Deep Learning

Somvanshi, S., Das, S., Javed, S. A., Antariksa, G., & Hossain, A. (2026) · ACM Computing Surveys · doi:10.1145/3807777 · Cited by 13

Topic Transferable Table Question Answering

Chemmengath, S., Kumar, V., Bharadwaj, S., Sen, J., Canim, M., Chakrabarti, S., Gliozzo, A., & Sankaranarayanan, K. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.342 · Cited by 10

Few-Shot Tabular Data Enrichment Using Fine-Tuned Transformer Architectures

Harari, A., & Katz, G. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.111 · Cited by 10

Learning to Generate Programs for Table Fact Verification via Structure-Aware Semantic Parsing

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