TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
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Recent years have witnessed the burgeoning of pretrained language models (LMs) for textbased natural language (NL) understanding tasks.Such models are typically trained on free-form NL text, hence may not be suitable for tasks like semantic parsing over structured data, which require reasoning over both free-form NL questions and structured tabular data (e.g., database tables).In this paper we present TABERT, a pretrained LM that jointly learns representations for NL sentences and (semi-)structured tables.TABERT is trained on a large corpus of 26 million tables and their English contexts.In experiments, neural semantic parsers using TABERT as feature representation layers achieve new best results on the challenging weakly-supervised semantic parsing benchmark WIKITABLEQUESTIONS, while performing competitively on the text-to-SQL dataset SPIDER. 1
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Transformer with global receptive field can effectively learn global representation and long-range dependency without absolute position information, achieving excellent performance on non-sequential data such as images and tables.
“Transformer with global receptive field could effectively learn global representation and longrange dependency without absolute position information, achieving excellent performance on nonsequential data (i.e., images, tables) 24,27 .”
From scBERT as a Large-scale Pretrained Deep Language Model for Cell Type Annotation of Single-cell RNA-seq Data (2021) · cites this paper as [27]
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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 112 · see citing works
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 111 · see citing works
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 109 · see citing works
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 107 · see citing works
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 100 · see citing works
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 99 · see citing works
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 · see citing works
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 95 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 81 · see citing works
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 77 · see citing works
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 78 · see citing works
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 73 · see citing works
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 72 · see citing works
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 71 · see citing works
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 71 · see citing works
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 70 · see citing works
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 · see citing works
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 69 · see citing works
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 66 · see citing works
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 64 · see citing works
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 63 · see citing works
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 58 · see citing works
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 57 · see citing works
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 · see citing works
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 54 · see citing works
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 53 · see citing works
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 52 · see citing works
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 52 · see citing works
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 49 · see citing works
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 49 · see citing works
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 · see citing works
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 47 · see citing works
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 47 · see citing works
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 46 · see citing works
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 46 · see citing works
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 45 · see citing works
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 44 · see citing works
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 42 · see citing works
Self-citationMagneto: 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 40 · see citing works
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 38 · see citing works
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 38 · see citing works
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 37 · see citing works
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 37 · see citing works
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 37 · see citing works
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 37 · see citing works
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 35 · see citing works
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 35 · see citing works
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 35 · see citing works
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 34 · see citing works
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 34 · see citing works
Introduction to Neural Transfer Learning With Transformers for Social Science Text Analysis
Wankmüller, S. (2022) · Sociological Methods & Research · doi:10.1177/00491241221134527 · Cited by 34 · see citing works
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 33 · see citing works
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 · see citing works
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 31 · see citing works
Self-citationMulti-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 31 · see citing works
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 31 · see citing works
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 · see citing works
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 30 · see citing works
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 29 · see citing works
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 · see citing works
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 27 · see citing works
Chen, C.-C., Huang, H., & Chen, H. (2021) · OAPEN (The OAPEN Foundation) · Cited by 27 · see citing works
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 · see citing works
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 26 · see citing works
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 · see citing works
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 26 · see citing works
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 26 · see citing works
- Authors
- Yin, P., Neubig, G., Yih, W.-T., & Riedel, S.
- Affiliations
- Meta (United States) (US)
- Venue
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Published
- 2020
- Pages
- 8413-8426
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W3035231859
- Cited by
- 487
- References
- 58
- Citations by year
- 2026: 68 · 2025: 86 · 2024: 72 · 2023: 89 · 2022: 76 · 2021: 84 · 2020: 12
- FWCI
- 24.93
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- pre-trained language models, tabular data, semantic parsing, structured data, natural language understanding, text-to-SQL, spiders, database tables
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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