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

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676

A primer in BERTology: What we know about how BERT works

Rogers, A., Kovaleva, O., & Rumshisky, A. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00349 · Cited by 1,552

Deep Neural Networks and Tabular Data: A Survey

Borisov, V., Leemann, T., Seßler, K., Haug, J., Pawelczyk, M., & Kasneci, G. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3229161 · Cited by 1,400

scBERT as a Large-scale Pretrained Deep Language Model for Cell Type Annotation of Single-cell RNA-seq Data

Yang, F., Wang, W., Wang, F., Fang, Y., Tang, D., Huang, J., Lu, H., & Yao, J. (2021) · doi:10.1101/2021.12.05.471261 · Cited by 617

Deep entity matching with pre-trained language models

Li, Y., Li, J., Suhara, Y., Doan, A., & Tan, W.-C. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3421424.3421431 · Cited by 544

Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation

Gao, D., Wang, H., Li, Y., Sun, X., Qian, Y., Ding, B., & Zhou, J. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3641204.3641221 · Cited by 342

SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-TrainingPreprint

Somepalli, G., Goldblum, M., Schwarzschild, A., Bruss, C. B., & Goldstein, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.01342 · Cited by 536

UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models

Xie, T., Wu, C. H., Shi, P., Zhong, R., Scholak, T., Yasunaga, M., Wu, C.-S., Zhong, M., Yin, P., Wang, S. I., et al. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.39 · Cited by 271

A Benchmark for Data Imputation Methods

Jäger, S., Allhorn, A., & Bießmann, F. (2021) · Frontiers in Big Data · doi:10.3389/fdata.2021.693674 · Cited by 268

A survey on deep learning approaches for text-to-SQL

Katsogiannis-Meimarakis, G., & Koutrika, G. (2023) · The VLDB Journal · doi:10.1007/s00778-022-00776-8 · Cited by 235

Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing

Lin, X., Socher, R., & Xiong, C. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.438 · Cited by 231

TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance

Zhu, F., Lei, W., Huang, Y., Wang, C., Zhang, S., Lv, J., Feng, F., & Chua, T.-S. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.254 · Cited by 193

Next-Generation Database Interfaces: A Survey of LLM-Based Text-to-SQL

Hong, Z., Yuan, Z., Zhang, Q., Chen, H., Dong, J., Huang, F., & Huang, X. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3609486 · Cited by 298

Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer

Powalski, R., Borchmann, Ł., Jurkiewicz, D., Dwojak, T., Pietruszka, M., & Pałka, G. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86331-9_47 · Cited by 174

CodeS: Towards Building Open-source Language Models for Text-to-SQL

Li, H., Zhang, J., Liu, H., Fan, J., Zhang, X., Zhu, J., Wei, R., Pan, H., Li, C., & Chen, H. (2024) · Proceedings of the ACM on Management of Data · doi:10.1145/3654930 · Cited by 241

TURL

Deng, X., Sun, H., Lees, A., Wu, Y., & Yu, C. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3430915.3430921 · Cited by 198

Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study

Sui, Y., Zhou, M., Zhou, M., Han, S., & Zhang, D. (2024) · Proceedings of the 17th ACM International Conference on Web Search and Data Mining · doi:10.1145/3616855.3635752 · Cited by 155

A Survey of Text-to-SQL in the Era of LLMs: Where Are We, and Where Are We Going?

Liu, X., Shen, S., Li, B., Ma, P., Jiang, R., Zhang, Y., Fan, J., Li, G., Tang, N., & Luo, Y. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3592032 · Cited by 257

Untitled work

(2021) · doi:10.18653/v1/2021.naacl-main.105 · Cited by 148

TAPEX: Table Pre-training via Learning a Neural SQL ExecutorPreprint

Liu, Q., Chen, B., Guo, J., Ziyadi, M., Lin, Z., Chen, W., & Lou, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.07653 · Cited by 145

URLTran: Improving Phishing URL Detection Using Transformers

Maneriker, P., Stokes, J. W., Lazo, E. G., Carutasu, D., Tajaddodianfar, F., & Gururajan, A. (2021) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom52596.2021.9653028 · Cited by 154

TABBIE: Pretrained Representations of Tabular Data

Iida, H., Thai, D., Manjunatha, V., & Iyyer, M. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.270 · Cited by 151

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

TUTA

Wang, Z., Dong, H., Jia, R., Li, J., Fu, Z., Han, S., & Zhang, D. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467434 · Cited by 153

CoTexT: Multi-task Learning with Code-Text Transformer

Phan, L., Tran, H., Le, D. X., Nguyen, H., Anibal, J., Peltekian, A., & Ye, Y. (2021) · doi:10.18653/v1/2021.nlp4prog-1.5 · Cited by 147

Table-GPT: Table Fine-tuned GPT for Diverse Table Tasks

Li, P., He, Y., Yashar, D., Cui, W., Song, G., Zhang, H., Fainman, D. R., Zhang, D., & Chaudhuri, S. (2024) · Proceedings of the ACM on Management of Data · doi:10.1145/3654979 · Cited by 194

How Large Language Models Will Disrupt Data Management

Fernandez, R. C., Elmore, A. J., Franklin, M. J. M., Krishnan, S., & Tan, C. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3611479.3611527 · Cited by 134

TURL

Deng, X., Sun, H., Lees, A., Wu, Y., & Yu, C. (2022) · ACM SIGMOD Record · doi:10.1145/3542700.3542709 · Cited by 141

FeTaQA: Free-form Table Question Answering

Nan, L., Hsieh, C., Mao, Z., Lin, X., Verma, N., Zhang, R., Kryściński, W., Schoelkopf, H., Kong, R., Tang, X., et al. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00446 · Cited by 150

Large Language Models are few(1)-shot Table Reasoners

Chen, W. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.83 · Cited by 170

Large Language Models are Versatile Decomposers: Decomposing Evidence and Questions for Table-based Reasoning

Ye, Y., Hui, B., Yang, M., Li, B., Huang, F., & Li, Y. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3591708 · Cited by 170

LI-EMRSQL: Linking Information Enhanced Text2SQL Parsing on Complex Electronic Medical Records

Li, Q., You, T., Chen, J., Zhang, Y., & Du, C. (2023) · IEEE Transactions on Reliability · doi:10.1109/tr.2023.3336330 · Cited by 143

Open Question Answering over Tables and TextPreprint

Chen, W., Chang, M.-W., Schlinger, E., Wang, W. Y., & Cohen, W. W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.10439 · Cited by 151

A Comprehensive Survey of Foundation Models in Medicine

Khan, W., Leem, S., See, K. B., Wong, J. K., Zhang, S., & Fang, R. (2025) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2025.3531360 · Cited by 137

SmBoP: Semi-autoregressive Bottom-up Semantic Parsing

Rubin, O., & Berant, J. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.29 · Cited by 109

Annotating Columns with Pre-trained Language Models

Suhara, Y., Li, J., Li, Y., Zhang, D., Demiralp, Ç., Chen, C., & Tan, W.-C. (2022) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3514221.3517906 · Cited by 128

Transformers for Tabular Data Representation: A Survey of Models and Applications

Badaro, G., Saeed, M., & Papotti, P. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00544 · Cited by 96

Tabular Transformers for Modeling Multivariate Time Series

Padhi, I., Schiff, Y., Melnyk, I., Rigotti, M., Mroueh, Y., Dognin, P., Ross, J., Nair, R., & Altman, E. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414142 · Cited by 109

DUET: Cross-Modal Semantic Grounding for Contrastive Zero-Shot Learning

Chen, Z., Huang, Y., Chen, J., Geng, Y., Zhang, W., Fang, Y., Pan, J. Z., & Chen, H. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i1.25114 · Cited by 91

TableFormer: Robust Transformer Modeling for Table-Text Encoding

Yang, J., Gupta, A., Upadhyay, S., He, L., Goel, R., & Paul, S. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.40 · Cited by 85

TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition

Xue, W., Yu, B., Wang, W., Tao, D., & Li, Q. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00133 · Cited by 81

UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering

Oğuz, B., Chen, X., Karpukhin, V., Peshterliev, S., Okhonko, D., Schlichtkrull, M., Gupta, S., Mehdad, Y., & Yih, S. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.115 · Cited by 89

Large language model for table processing: a survey

Lu, W., Zhang, J., Fan, J., Fu, Z., Chen, Y., & Du, X. (2025) · Frontiers of Computer Science · doi:10.1007/s11704-024-40763-6 · Cited by 107

DePlot: One-shot visual language reasoning by plot-to-table translation

Liu, F., Eisenschlos, J. M., Piccinno, F., Krichene, S., Pang, C., Lee, K., Joshi, M., Chen, W., Collier, N., & Altün, Y. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.660 · Cited by 101

Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training

Shi, P., Ng, P., Wang, Z., Zhu, H., Li, A. H., Wang, J., dos Santos, C. N., & Xiang, B. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17627 · Cited by 75

Dual-objective fine-tuning of BERT for entity matching

Peeters, R., & Bizer, C. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3467861.3467878 · Cited by 80

Meta-Learning for Domain Generalization in Semantic Parsing

Wang, B., Lapata, M., & Titov, I. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.33 · Cited by 79

DeepJoin: Joinable Table Discovery with Pre-Trained Language Models

Dong, Y., Xiao, C., Nozawa, T., Enomoto, M., & Oyamada, M. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3603581.3603587 · Cited by 99

S2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers

Hui, B., Geng, R., Wang, L., Qin, B., Li, Y., Li, B., Sun, J., & Li, Y. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.99 · Cited by 84

MATE: Multi-view Attention for Table Transformer Efficiency

Eisenschlos, J. M., Gor, M., Müller, T., & Cohen, W. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.600 · Cited by 69

Enhancing Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies

Nan, L., Zhao, Y., Zou, W., Ri, N., Tae, J., Zhang, E., Cohan, A., & Radev, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.996 · Cited by 102

RPT

Tang, N., Fan, J., Li, F., Tu, J., Du, X., Li, G., Madden, S. R., & Ouzzani, M. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3457390.3457391 · Cited by 80

From tabular data to knowledge graphs: A survey of semantic table interpretation tasks and methods

Liu, J., Chabot, Y., Raphaël Troncy, A., Huynh, V.-P., Labbé, T., & Monnin, P. (2022) · Journal of Web Semantics · doi:10.1016/j.websem.2022.100761 · Cited by 81

HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation

Cheng, Z., Dong, H., Wang, Z., Jia, R., Guo, J., Gao, Y., Han, S., Lou, J., & Zhang, D. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.78 · Cited by 86

GitTables: A Large-Scale Corpus of Relational Tables

Hulsebos, M., Demiralp, Ç., & Groth, P. (2023) · Proceedings of the ACM on Management of Data · doi:10.1145/3588710 · Cited by 89

Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks

Dong, H., Cheng, Z., He, X., Zhou, M., Zhou, A., Zhou, F., Liu, A., Han, S., & Zhang, D. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/761 · Cited by 73

OmniSQL: Synthesizing High-Quality Text-to-SQL Data at Scale

Li, H., Wu, S., Zhang, X., Huang, X., Zhang, J., Jiang, F., Wang, S., Zhang, T., Chen, J., Shi, R., et al. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3749646.3749723 · Cited by 56

ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser

Chen, Z., Chen, L., Zhao, Y., Cao, R., Xu, Z., Zhu, S., & Yu, K. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.441 · Cited by 59

Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey

Zhang, W., Wang, Y., Song, Y., Wei, V. J., Tian, Y., Qi, Y., Chan, J. H., Wong, R. C.-W., & Yang, H. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3400824 · Cited by 66

Retrieving Complex Tables with Multi-Granular Graph Representation Learning

Wang, F., Sun, K., Chen, M., Pujara, J., & Szekely, P. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462909 · Cited by 61

Chorus: Foundation Models for Unified Data Discovery and Exploration

Kayali, M., Lykov, A., Fountalis, I., Vasiloglou, N., Olteanu, D., & Suciu, D. M. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3659437.3659461 · Cited by 70

SmBoP: Semi-autoregressive Bottom-up Semantic Parsing

Rubin, O., & Berant, J. (2021) · Workshop on Structured Prediction for NLP (SPNLP) · doi:10.18653/v1/2021.spnlp-1.2 · Cited by 50

SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation LearningPreprint

Uçar, T., Hajiramezanali, E., & Edwards, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.04361 · Cited by 54

SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing

Yu, T., Zhang, R., Polozov, A., Meek, C., & Awadallah, A. H. (2021) · International Conference on Learning Representations · Cited by 47

SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation

Li, Z., Kim, J., Chiang, Y., & Chen, M. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.200 · Cited by 59

TCN: Table Convolutional Network for Web Table Interpretation

Wang, D., Shiralkar, P., Lockard, C., Huang, B., Dong, X. L., & Jiang, M. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3450090 · Cited by 53

Capturing Row and Column Semantics in Transformer Based Question Answering over Tables

Glass, M., Canim, M., Gliozzo, A., Chemmengath, S., Kumar, V., Chakravarti, R., Sil, A., Pan, F., Bharadwaj, S., & Fauceglia, N. R. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.96 · Cited by 51

SemEval-2021 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS)

Wang, N. X. R., Mahajan, D., Danilevsky, M., & Rosenthal, S. (2021) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2021.semeval-1.39 · Cited by 51

A Deep Dive into Deep Learning Approaches for Text-to-SQL Systems

Katsogiannis-Meimarakis, G., & Koutrika, G. (2021) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3448016.3457543 · Cited by 50

Representation Learning for Tabular Data: A Comprehensive Survey

Jiang, J., Liu, S.-Y., Cai, H.-R., Zhou, Q.-L., & Ye, H.-J. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3657217 · Cited by 74

OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering

Jiang, Z., Mao, Y., He, P., Neubig, G., & Chen, W. (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.68 · Cited by 51

Magneto: Combining Small and Large Language Models for Schema Matching

Liu, Y., Pena, E. H. M., Santos, A. S. R., Wu, E., & Freire, J. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3742728.3742757 · Cited by 62

DeepTLF: robust deep neural networks for heterogeneous tabular data

Borisov, V., Broelemann, K., Kasneci, E., & Kasneci, G. (2022) · International Journal of Data Science and Analytics · doi:10.1007/s41060-022-00350-z · Cited by 40

Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning

Wang, F., Xu, Z., Szekely, P. A., & Chen, M. (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.371 · Cited by 42

Open Domain Question Answering with A Unified Knowledge Interface

Ma, K., Cheng, H., Liu, X., Nyberg, E., & Gao, J. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.113 · Cited by 41

NL-EDIT: Correcting Semantic Parse Errors through Natural Language Interaction

Elgohary, A., Meek, C., Richardson, M., Fourney, A., Ramos, G., & Awadallah, A. H. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.444 · Cited by 49

AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry

Katsis, Y., Chemmengath, S., Kumar, V., Bharadwaj, S., Canim, M., Glass, M., Gliozzo, A., Pan, F., Sen, J., Sankaranarayanan, K., et al. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-industry.34 · Cited by 43

StruBERT: Structure-aware BERT for Table Search and Matching

Trabelsi, M., Chen, Z., Zhang, S., Davison, B. D., & Heflin, J. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3511972 · Cited by 47

SwitchTab: Switched Autoencoders Are Effective Tabular Learners

Wu, J., Chen, S., Zhao, Q., Sergazinov, R., Chen, L., Liu, S., Zhao, C., Xie, T., Guo, H., Ji, C., et al. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i14.29523 · Cited by 49

A survey of table reasoning with large language models

Zhang, X., Wang, D., Dou, L., Zhu, Q., & Che, W. (2025) · Frontiers of Computer Science · doi:10.1007/s11704-024-40330-z · Cited by 46

XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing

Shi, P., Zhang, R., Bai, H., & Lin, J. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.384 · Cited by 46

Representations for Question Answering from Documents with Tables and Text

Zayats, V., Toutanova, K., & Mari Ostendorf, E. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.253 · Cited by 36

Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills

Yoran, O., Talmor, A., & Berant, J. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.416 · Cited by 40

Introduction to Neural Transfer Learning With Transformers for Social Science Text Analysis

Wankmüller, S. (2022) · Sociological Methods & Research · doi:10.1177/00491241221134527 · Cited by 45

Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing

Wu, K., Wang, L., Li, Z., Zhang, A., Xiao, X., Wu, H. R., Zhang, M., & Wang, H. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.707 · Cited by 39

LakeBench: A Benchmark for Discovering Joinable and Unionable Tables in Data Lakes

Deng, Y., Chai, C., Cao, L., Qin, Y., Chen, S., Yu, Y., Sun, Z., Wang, J., Li, J., Cao, Z., et al. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3659437.3659448 · Cited by 32

L2CEval : Evaluating Language-to-Code Generation Capabilities of Large Language Models

Ni, A., Yin, P., Zhao, Y., Riddell, M., Feng, T., Shen, R., Yin, S., Ye, L., Yavuz, S., Xiong, C., et al. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00705 · Cited by 49

Multi-modal large language models in radiology: principles, applications, and potential

Shen, Y., Xu, Y., Ma, J., Rui, W., Zhao, C., Heacock, L. C., & Huang, C. (2024) · Abdominal Radiology · doi:10.1007/s00261-024-04708-8 · Cited by 34

Incorporating External Knowledge to Enhance Tabular Reasoning

Neeraja, J., Gupta, V., & Srikumar, V. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.224 · Cited by 33

PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-training

Gu, Z., Fan, J., Tang, N., Nakov, P., Zhao, X., & Du, X. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.331 · Cited by 30

Generative Table Pre-training Empowers Models for Tabular Prediction

Zhang, T., Wang, S., Yan, S., Li, J., & Liu, Q. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.917 · Cited by 49

GenHPF: General Healthcare Predictive Framework for Multi-Task Multi-Source Learning

Hur, K., Oh, J., Kim, J., Kim, J., Lee, M. J., Cho, E., Moon, S.-E., Kim, Y., Atallah, L., & Choi, E. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3327951 · Cited by 35

STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing

Cai, Z., Li, X., Hui, B., Yang, M., Li, B., Li, B., Cao, Z., Li, W., Huang, F., Si, L., et al. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.89 · Cited by 28

Large Language Models are Complex Table Parsers

Zhao, B., Ji, C., Zhang, Y., He, W., Wang, Y., Wang, Q., Feng, R., & Zhang, X. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.914 · Cited by 35

9789811628818.pdf

Chen, C.-C., Huang, H., & Chen, H. (2021) · OAPEN (The OAPEN Foundation) · Cited by 28

NCART: Neural Classification and Regression Tree for tabular data

Luo, J., & Xu, S. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110578 · Cited by 26

Phishing Website Detection Through Multi-model Analysis of HTML Content

Çolhak, F., Ecevit, M. İ., Uçar, B. E., Creutzburg, R. M., & Dağ, H. (2025) · Algorithms for intelligent systems · doi:10.1007/978-981-97-6957-5_15 · Cited by 29

Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering

Li, A. H., Ng, P., Xu, P., Zhu, H., Wang, Z., & Xiang, B. (2021) · doi:10.18653/v1/2021.acl-long.315 · Cited by 26

FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining

Cheng, Z., Dong, H., Jia, R., Wu, P., Han, S., Cheng, F., & Zhang, D. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.82 · Cited by 29

Table-To-Text generation and pre-training with TabT5

Andrejczuk, E., Eisenschlos, J. M., Piccinno, F., Krichene, S., & Altün, Y. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.503 · Cited by 31

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