Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks
Open accessAbstract
Language models pretrained on text from a wide variety of sources form the foundation of today's NLP. In light of the success of these broad-coverage models, we investigate whether it is still helpful to tailor a pretrained model to the domain of a target task. We present a study across four domains (biomedical and computer science publications, news, and reviews) and eight classification tasks, showing that a second phase of pretraining in-domain (domain-adaptive pretraining) leads to performance gains, under both high- and low-resource settings. Moreover, adapting to the task's unlabeled data (task-adaptive pretraining) improves performance even after domain-adaptive pretraining. Finally, we show that adapting to a task corpus augmented using simple data selection strategies is an effective alternative, especially when resources for domain-adaptive pretraining might be unavailable. Overall, we consistently find that multi-phase adaptive pretraining offers large gains in task performance.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Domain adaptation studies show that task performance varies across domains, with general-purpose LLMs exhibiting stronger capabilities in well-represented areas such as Computer Vision than in specialized fields such as Quantum Computing, due to differences in pretraining corpus composition.
“Domain adaptation studies [23] show that task performance varies across domains, with general-purpose LLMs exhibiting stronger capabilities in well-represented areas such as Computer Vision than in specialized fields such as Quantum Computing, due to differences in pretraining corpus composition.”
From Abstention vs. Hallucination: Benchmarking LLM Source Attribution for Scientific Citations (2024) · cites this paper as [23]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredContinued pre-training on domain-specific data has been shown to be effective for improving performance in a specific domain.
“Continued pre-training on domainspecific data has been shown to be effective for improving performance in a specific domain (Gururangan et al., 2020).”
From The Llama 3 Herd of Models (2024) · cites this paper as Gururangan et al., 2020
Further pre-training has been shown to increase scores when adapting a model to a specific domain or task.
“Further pre-training has been shown to increase scores when adapting a model to a specific domain or task (Gururangan et al., 2020;Lee et al., 2020).”
From QueryNER: Segmentation of E-commerce Queries (2024) · cites this paper as Gururangan et al., 2020
TAPT and DAPT were proposed by Gururangan et al.; TAPT continues training on target task training data after pre-training RoBERTa, while DAPT pre-trains RoBERTa on datasets with minor domain differences from target tasks.
“Task adaptive pre-training (TAPT) and domain adaptive pre-training (DAPT) have been proposed by Gururangan et al. [37]. TAPT continues to train it on the target task's training dataset, after pre-training RoBERTa on large-scale corpora. DAPT is still pre-training RoBERTa on datasets with minor domain differences from datasets in target tasks.”
From Self-supervised learning: A succinct review (2023) · cites this paper as [37]
The method relies on a comparison of gridbox-scale trends in observational datasets for temperature and precipitation with those produced in climate model runs from CMIP6.
“The method relies on a comparison of gridbox-scale trends in observational datasets for temperature (HadCRUT4 version 4.6 54 ) and precipitation (GPCC v2018, obtainable from https://psl.noaa.gov/data/gridded/data.gpcc.html),with those produced in climate model runs from CMIP6 55 .”
From Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies (2021) · cites this paper as [54]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
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 48,293 · see citing works
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 · see citing works
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929 · see citing works
Character-level Convolutional Networks for Text ClassificationPreprint
Zhang, X., Zhao, J., & LeCun, Y. (2015) · Neural Information Processing Systems · doi:10.48550/arxiv.1509.01626 · Cited by 7,552 · see citing works
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz682 · Cited by 7,218 · see citing works
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 · see citing works
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al. (2016) · arXiv · doi:10.48550/arxiv.1609.08144 · Cited by 5,319 · see citing works
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019) · arXiv · doi:10.48550/arxiv.1910.01108 · Cited by 4,653 · see citing works
Billion-scale similarity search with GPUs
Johnson, J., Douze, M., & Jegou, H. (2021) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2019.2921572 · Cited by 4,228 · see citing works
Universal Language Model Fine-tuning for Text Classification
Howard, J., & Ruder, S. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1031 · Cited by 4,100 · see citing works
Learning Word Vectors for Sentiment Analysis
Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., & Potts, C. (2011) · Cited by 3,710 · see citing works
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
Williams, A., Nangia, N., & Bowman, S. (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/n18-1101 · Cited by 3,342 · see citing works
SciBERT: A Pretrained Language Model for Scientific Text
Beltagy, I., Lo, K., & Cohan, A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1371 · Cited by 3,218 · see citing works
Self-citationImage-Based Recommendations on Styles and Substitutes
McAuley, J., Targett, C., Shi, Q., & van den Hengel, A. (2015) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2766462.2767755 · Cited by 2,348 · see citing works
Longformer: The Long-Document Transformer
Beltagy, I., Peters, M. E., & Cohan, A. (2020) · arXiv · doi:10.48550/arxiv.2004.05150 · Cited by 2,281 · see citing works
Self-citationZhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., & Fidler, S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.11 · Cited by 2,264 · see citing works
Alsentzer, E., Murphy, J., Boag, W., Weng, W.-H., Jindi, D., Naumann, T., & McDermott, M. (2019) · Proceedings of the 2nd Clinical Natural Language Processing Workshop · doi:10.18653/v1/w19-1909 · Cited by 1,736 · see citing works
(2018) · International Conference on Learning Representations · Cited by 1,621 · see citing works
Focused crawling: a new approach to topic-specific Web resource discovery
Chakrabarti, S., van den Berg, M., & Dom, B. (1999) · Computer Networks · doi:10.1016/s1389-1286(99)00052-3 · Cited by 1,487 · see citing works
AllenNLP: A Deep Semantic Natural Language Processing Platform
Gardner, M., Grus, J., Neumann, M., Tafjord, O., Dasigi, P., Liu, N. F., Peters, M. E., Schmitz, M., & Zettlemoyer, L. (2018) · Workshop for NLP Open Source Software (NLP-OSS) · doi:10.18653/v1/w18-2501 · Cited by 1,273 · see citing works
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
Wang, A., Pruksachatkun, Y., Nangia, N., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. R. (2019) · arXiv · doi:10.48550/arxiv.1905.00537 · Cited by 1,051 · see citing works
ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing
Neumann, M., King, D., Beltagy, I., & Ammar, W. (2019) · BioNLP Workshop and Shared Task (BioNLP) · doi:10.18653/v1/w19-5034 · Cited by 772 · see citing works
Self-citationLuan, Y., He, L., Mari Ostendorf, E., & Hajishirzi, H. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1360 · Cited by 714 · see citing works
One billion word benchmark for measuring progress in statistical language modeling
Chelba, C., Mikolov, T., Schuster, M., Ge, Q., Brants, T., Koehn, P., & Robinson, T. (2014) · Interspeech 2014 · doi:10.21437/interspeech.2014-564 · Cited by 627 · see citing works
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
Huang, K., Altosaar, J., & Ranganath, R. (2019) · arXiv · doi:10.48550/arxiv.1904.05342 · Cited by 618 · see citing works
Intelligent Selection of Language Model Training Data
Moore, R. C., & Lewis, W. D. (2010) · Cited by 530 · see citing works
Lee, D. (2002) · BRILL eBooks · doi:10.1163/9789004334236_021 · Cited by 516 · see citing works
S2ORC: The Semantic Scholar Open Research Corpus
Lo, K., Wang, L. L., Neumann, M., Kinney, R., & Weld, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.447 · Cited by 457 · see citing works
Self-citationTo Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks
Peters, M. E., Ruder, S., & Smith, N. A. (2019) · Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019) · doi:10.18653/v1/w19-4302 · Cited by 432 · see citing works
Self-citationSentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data TasksPreprint
Phang, J., Févry, T., & Bowman, S. R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.01088 · Cited by 417 · see citing works
The CHEMDNER corpus of chemicals and drugs and its annotation principles
Krallinger, M., Rabal, O., Leitner, F., Vázquez, M., Salgado, D., Lu, Z., Leaman, R., Lu, Y., Ji, D., Lowe, D. M., et al. (2015) · Journal of Cheminformatics · doi:10.1186/1758-2946-7-s1-s2 · Cited by 406 · see citing works
BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis
Xu, H., Liu, B., Shu, L., & Yu, P. S. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1242 · Cited by 308 · see citing works
A Simple Method for Commonsense Reasoning
Trinh, T. H., & Le, Q. V. (2018) · arXiv · doi:10.48550/arxiv.1806.02847 · Cited by 293 · see citing works
Zero-Shot Entity Linking by Reading Entity Descriptions
Logeswaran, L., Chang, M.-W., Lee, K., Toutanova, K., Devlin, J., & Lee, H. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1335 · Cited by 254 · see citing works
Diffusion of Lexical Change in Social Media
Eisenstein, J., O'Connor, B., Smith, N. A., & Xing, E. P. (2014) · PLoS ONE · doi:10.1371/journal.pone.0113114 · Cited by 245 · see citing works
Self-citationShow Your Work: Improved Reporting of Experimental Results
Dodge, J., Gururangan, S., Card, D., Schwartz, R., & Smith, N. A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1224 · Cited by 233 · see citing works
Self-citationCloze-driven Pretraining of Self-attention Networks
Baevski, A., Edunov, S., Liu, Y., Zettlemoyer, L., & Auli, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1539 · Cited by 215 · see citing works
Measuring the Evolution of a Scientific Field through Citation Frames
Jurgens, D., Kumar, S., Hoover, R., McFarland, D., & Jurafsky, D. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00028 · Cited by 212 · see citing works
Unsupervised Domain Clusters in Pretrained Language Models
Aharoni, R., & Goldberg, Y. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.692 · Cited by 181 · see citing works
Learning and Evaluating General Linguistic IntelligencePreprint
Yogatama, D., d’Autume, C. D. M., Connor, J. T., Kočiský, T., Chrzanowski, M., Kong, L., Lazaridou, A., Wang, L., Yu, L., Dyer, C., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.11373 · Cited by 171 · see citing works
Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling
Han, X., & Eisenstein, J. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1433 · Cited by 149 · see citing works
Sentiment Classification Using Document Embeddings Trained with Cosine Similarity
Thongtan, T., & Phienthrakul, T. (2019) · Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (ACL SRW) · doi:10.18653/v1/p19-2057 · Cited by 136 · see citing works
Pretrained Language Models for Sequential Sentence Classification
Cohan, A., Beltagy, I., King, D., Dalvi, B. B., & Weld, D. S. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1383 · Cited by 125 · see citing works
Self-citationCurriculum Learning for Domain Adaptation in Neural Machine Translation
Zhang, X., Shapiro, P., Kumar, G., McNamee, P., Carpuat, M. J., & Duh, K. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1189 · Cited by 112 · see citing works
An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models
Chronopoulou, A., Baziotis, C., & Potamianos, A. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1213 · Cited by 105 · see citing works
Variational Pretraining for Semi-supervised Text Classification
Gururangan, S., Dang, T., Card, D., & Smith, N. A. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1590 · Cited by 95 · see citing works
Self-citationHow Additional Knowledge can Improve Natural Language Commonsense Question Answering?Preprint
Mitra, A., Banerjee, P., Pal, K. K., Mishra, S. R., & Baral, C. R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.08855 · Cited by 45 · see citing works
IMHO Fine-Tuning Improves Claim Detection
Chakrabarty, T., Hidey, C., & McKeown, K. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1054 · Cited by 38 · see citing works
Using Similarity Measures to Select Pretraining Data for NER
Dai, X., Karimi, S., Hachey, B., & Paris, C. L. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1149 · Cited by 36 · see citing works
Learning to select data for transfer learning with Bayesian Optimization
Ruder, S., & Plank, B. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1038 · Cited by 31 · see citing works
Mitra, A., Banerjee, P., Pal, K. K., Mishra, S. R., & Baral, C. R. (2019) · arXiv (Cornell University) · Cited by 29 · see citing works
What’s in a Domain? Analyzing Genre and Topic Differences in Statistical Machine Translation
van der Wees, M., Bisazza, A., Weerkamp, W., & Monz, C. (2015) · doi:10.3115/v1/p15-2092 · Cited by 22 · see citing works
Review Conversational Reading ComprehensionPreprint
Xu, H., Liu, B., Shu, L., & Yu, P. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.00821 · Cited by 17 · see citing works
Shallow Syntax in Deep WaterPreprint
Swayamdipta, S., Peters, M. E., Roof, B., Dyer, C., & Smith, N. A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.11047 · Cited by 10 · see citing works
Self-citationTowards a continuous modeling of natural language domains
Ruder, S., Ghaffari, P., & Breslin, J. G. (2016) · doi:10.18653/v1/w16-6012 · Cited by 5 · see citing works
Flagged first, then by citations
5 of the 62 references couldn’t be matched to a known paper.
Research works printed but not linked: 8
- Franck Dernoncourt and Ji Young Lee. 2017. Pubmed 200k RCT: a dataset for sequential sentence classification in medical abstracts. In IJCNLP . https://www.aclweb.org/anthology/I17-2052/
- Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In WWW . https://arxiv.org/abs/1602.01585 · arXiv
- Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. 2020. Generalization through memorization: Nearest neighbor language models. In ICLR . To appear.
- Martin Krallinger, Obdulia Rabal, Saber Ahmad Akhondi, Mart´ ın P´ erez P´ erez, J´ es ´ us L´ opez Santamar´ ıa, Gael P´ erez Rodr´ ıguez, Georgios Tsatsaronis, Ander Intxaurrondo, Jos´ e Antonio Baso L´ opez, Umesh Nandal, E. M. van Buel, A. Poorna Chandrasekhar, Marleen Rodenburg, Astrid Lægreid, Marius A. Doornenbal, Julen Oyarz´ abal, An´ alia Lourenc ¸o, and Alfonso Valencia. 2017. Overview of the biocreative vi chemical-protein interaction track. In Proceedings of the BioCreative VI Workshop .
- Barbara Plank. 2016. What to do about non-standard (or non-canonical) language in NLP. In KONVENS . https://arxiv.org/abs/1608.07836 · arXiv
- Steffen Remus and Chris Biemann. 2016. DomainSpecific Corpus Expansion with Focused Webcrawling. In LREC .
- Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R´ emi Louf, Morgan Funtowicz, and Jamie Brew. 2019. HuggingFace's Transformers: State-of-the-art natural language processing. arXiv:1910.03771. · arXiv
- Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2019. Defending against neural fake news. In NeurIPS .
Cited by
Sorted by citations
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 · see citing works
Text Data Augmentation for Deep Learning
Shorten, C., Khoshgoftaar, T. M., & Furht, B. (2021) · Journal Of Big Data · doi:10.1186/s40537-021-00492-0 · Cited by 1,798 · see citing works
mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer
Xue, L., Constant, N., Roberts, A., Kale, M., Al-Rfou, R., Siddhant, A., Barua, A., & Raffel, C. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.41 · Cited by 1,629 · see citing works
Pre-trained models for natural language processing: A survey
Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N., & Huang, X. (2020) · Science China Technological Sciences · doi:10.1007/s11431-020-1647-3 · Cited by 1,591 · see citing works
scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI
Cui, H., Wang, C., Maan, H., Pang, K., Luo, F., & Wang, B. (2023) · doi:10.1101/2023.04.30.538439 · Cited by 1,383 · see citing works
A Survey of AI-Generated Content (AIGC)
Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P. S., & Sun, L. (2024) · ACM Computing Surveys · doi:10.1145/3704262 · Cited by 1,099 · see citing works
Pre-trained models: Past, present and future
Han, X., Zhang, Z., Ding, N., Gu, Y., Liu, X., Huo, Y., Qiu, J., Yao, Y., Zhang, A., Zhang, L., et al. (2021) · AI Open · doi:10.1016/j.aiopen.2021.08.002 · Cited by 994 · see citing works
The rise and potential of large language model based agents: a survey
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., et al. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4222-0 · Cited by 970 · see citing works
BERTweet: A pre-trained language model for English Tweets
Nguyen, D. Q., Vu, T., & Nguyen, A. T. (2020) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2020.emnlp-demos.2 · Cited by 949 · see citing works
mT5: A massively multilingual pre-trained text-to-text transformerPreprint
Xue, L., Constant, N., Roberts, A., Kale, M., Al‐Rfou, R., Siddhant, A., Barua, A., & Raffel, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.11934 · Cited by 852 · see citing works
Jin, D., Pan, E., Oufattole, N., Weng, W.-H., Fang, H., & Szolovits, P. (2021) · Applied Sciences · doi:10.3390/app11146421 · Cited by 718 · see citing works
BERTimbau: Pretrained BERT Models for Brazilian Portuguese
Souza, F., Nogueira, R. F., & Lotufo, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61377-8_28 · Cited by 702 · see citing works
RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models
Gehman, S., Gururangan, S., Sap, M., Choi, Y., & Smith, N. A. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.301 · Cited by 693 · see citing works
Self-citationSUPERB: Speech Processing Universal PERformance Benchmark
Yang, S.-W., Chi, P.-H., Chuang, Y.-S., Lai, C.-I. J., Lakhotia, K., Lin, Y. Y., Liu, A. T., Shi, J., Chang, X., Lin, G.-T., et al. (2021) · Interspeech 2021 · doi:10.21437/interspeech.2021-1775 · Cited by 681 · see citing works
AdapterHub: A Framework for Adapting Transformers
Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., & Gurevych, I. (2020) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2020.emnlp-demos.7 · Cited by 506 · see citing works
Large Language Models in Medicine: The Potentials and Pitfalls
Omiye, J. A., Gui, H., Rezaei, S. J., Zou, J., & Daneshjou, R. (2024) · Annals of Internal Medicine · doi:10.7326/m23-2772 · Cited by 394 · see citing works
Fact or Fiction: Verifying Scientific Claims
Wadden, D., Lin, S., Lo, K., Wang, L. L., van Zuylen, M., Cohan, A., & Hajishirzi, H. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.609 · Cited by 382 · see citing works
Self-citationHateBERT: Retraining BERT for Abusive Language Detection in English
Caselli, T., Basile, V., Mitrović, J., & Granitzer, M. (2021) · Workshop on Online Abuse and Harms (WOAH) · doi:10.18653/v1/2021.woah-1.3 · Cited by 373 · see citing works
MatSciBERT: A materials domain language model for text mining and information extraction
Gupta, T., Zaki, M. F. M., Krishnan, N. M. A., & Mausam, M. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00784-w · Cited by 361 · see citing works
Deep Learning applications for COVID-19
Shorten, C., Khoshgoftaar, T. M., & Furht, B. (2021) · Journal of Big Data · doi:10.1186/s40537-020-00392-9 · Cited by 350 · see citing works
A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios
Hedderich, M. A., Lange, L., Adel, H., Strötgen, J., & Klakow, D. (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.201 · Cited by 310 · see citing works
Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus
Dodge, J., Sap, M., Marasović, A., Agnew, W., Ilharco, G., Groeneveld, D., Mitchell, M., & Gardner, M. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.98 · Cited by 306 · see citing works
Self-citationMeasuring Mathematical Problem Solving With the MATH Dataset
Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., & Steinhardt, J. (2021) · arXiv · doi:10.48550/arxiv.2103.03874 · Cited by 306 · see citing works
Lawformer: A pre-trained language model for Chinese legal long documents
Xiao, C., Hu, X., Liu, Z., Tu, C., & Sun, M. (2021) · AI Open · doi:10.1016/j.aiopen.2021.06.003 · Cited by 301 · see citing works
Self-supervised learning: A succinct review
Rani, V., Nabi, S. T., Kumar, M., Mittal, A., & Kumar, K. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09884-2 · Cited by 295 · see citing works
Large pre-trained language models contain human-like biases of what is right and wrong to do
Schramowski, P., Turan, C., Andersen, N., Rothkopf, C. A., & Kersting, K. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00458-8 · Cited by 291 · see citing works
Auditing large language models: a three-layered approach
Mökander, J., Schuett, J., Kirk, H. R., & Floridi, L. (2024) · AI and Ethics · doi:10.1007/s43681-023-00289-2 · Cited by 289 · see citing works
Should ChatGPT be biased? Challenges and risks of bias in large language models
Ferrara, E. (2023) · First Monday · doi:10.5210/fm.v28i11.13346 · Cited by 282 · see citing works
AMMU: A survey of transformer-based biomedical pretrained language models
Kalyan, K. S., Rajasekharan, A., & Sangeetha, S. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103982 · Cited by 280 · see citing works
MasakhaNER: Named Entity Recognition for African Languages
Adelani, D. I., Abbott, J., Neubig, G., D’souza, D., Kreutzer, J., Lignos, C., Palen-Michel, C., Buzaaba, H., Rijhwani, S., Ruder, S., et al. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00416 · Cited by 277 · see citing works
Pretrained Transformers for Text Ranking: BERT and Beyond
Lin, J., Nogueira, R. F., & Yates, A. (2021) · Synthesis lectures on human language technologies · doi:10.2200/s01123ed1v01y202108hlt053 · Cited by 273 · see citing works
A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support
Sharma, A., Miner, A., Atkins, D., & Althoff, T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.425 · Cited by 268 · see citing works
Neural Unsupervised Domain Adaptation in NLP—A Survey
Ramponi, A., & Plank, B. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.603 · Cited by 253 · see citing works
Domain Adaptation: Challenges, Methods, Datasets, and Applications
Singhal, P., Walambe, R. A., Ramanna, S., & Kotecha, K. V. (2023) · IEEE Access · doi:10.1109/access.2023.3237025 · Cited by 253 · see citing works
Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies
Callaghan, M., Schleussner, C.-F., Nath, S., Lejeune, Q., Knutson, T. R., Reichstein, M., Hansen, G., Theokritoff, E., Andrijevic, M., Brecha, R. J., et al. (2021) · Nature Climate Change · doi:10.1038/s41558-021-01168-6 · Cited by 251 · see citing works
Large AI Models in Health Informatics: Applications, Challenges, and the Future
Qiu, J., Li, L., Sun, J., Peng, J., Shi, P., Zhang, R., Dong, Y., Lam, K., Lo, F. P.-W., Xiao, B. J., et al. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3316750 · Cited by 246 · see citing works
AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language ProcessingPreprint
Kalyan, K. S., Rajasekharan, A., & Sangeetha, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.05542 · Cited by 240 · see citing works
MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare
Ji, S., Zhang, T., Ansari, L., Fu, J., Tiwari, P., & Cambria, E. (2022) · Proceedings of the Language Resources and Evaluation Conference · doi:10.63317/2d7umxifj8wz · Cited by 233 · see citing works
CLIMATEBERT: A Pretrained Language Model for Climate-Related TextPreprint
Webersinke, N., Kraus, M., Bingler, J., & Leippold, M. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4229146 · Cited by 231 · see citing works
Can Foundation Models Wrangle Your Data?
Narayan, A., Chami, I., Orr, L., & Ré, C. (2022) · Proceedings of the VLDB Endowment · doi:10.14778/3574245.3574258 · Cited by 229 · see citing works
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
Li, C., Wong, C., Zhang, S., Usuyama, N., Liu, H., Yang, J., Naumann, T., Poon, H., & Gao, J. (2023) · arXiv · doi:10.48550/arxiv.2306.00890 · Cited by 590 · see citing works
“Similar to the don’t stop pre-training argument in [12], we consider a scalable pipeline to create domain-specific instruct data from large unlabelled data, and advocate don’t stop instruction-tuning to build customized LMM. ( iii ) Low serving cost.”
BackgroundSelf-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP
Schick, T., Udupa, S., & Schütze, H. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00434 · Cited by 227 · see citing works
Singh, R., Sledzieski, S., Bryson, B. D., Cowen, L., & Berger, B. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2220778120 · Cited by 207 · see citing works
An Analysis of Simple Data Augmentation for Named Entity Recognition
Dai, X., & Adel, H. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.343 · Cited by 206 · see citing works
Pre-trained Language Models in Biomedical Domain: A Systematic Survey
Wang, B., Xie, Q., Pei, J., Chen, Z., Tiwari, P., Li, Z., & Fu, J. (2023) · ACM Computing Surveys · doi:10.1145/3611651 · Cited by 206 · see citing works
COVIDLies: Detecting COVID-19 Misinformation on Social Media
Hossain, T., Logan, R. L., Ugarte, D. A., Matsubara, Y., Young, S. D., & Singh, S. (2020) · Workshop on NLP for COVID-19 (NLP-COVID19) · doi:10.18653/v1/2020.nlpcovid19-2.11 · Cited by 204 · see citing works
Organisciak, P., Acar, S., Dumas, D., & Berthiaume, K. (2023) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2023.101356 · Cited by 202 · see citing works
Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited
Yuan, Z., Yuan, F., Song, Y., Li, Y., Fu, J., Yang, F., Pan, Y., & Ni, Y. (2023) · Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3539618.3591932 · Cited by 200 · see citing works
DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts
Liu, A., Sap, M., Lu, X., Swayamdipta, S., Bhagavatula, C., Smith, N. A., & Choi, Y. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.522 · Cited by 199 · see citing works
Self-citationChristou, P. (2023) · The Qualitative Report · doi:10.46743/2160-3715/2023.6406 · Cited by 192 · see citing works
Continual Lifelong Learning in Natural Language Processing: A Survey
Biesialska, M., Biesialska, K., & Costa-jussà, M. R. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.574 · Cited by 190 · see citing works
Knowledge Graph Prompting for Multi-Document Question Answering
Wang, Y., Lipka, N., Rossi, R. A., Siu, A. F., Zhang, R., & Derr, T. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29889 · Cited by 189 · see citing works
Wang, K., Reimers, N., & Gurevych, I. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.59 · Cited by 187 · see citing works
End-to-End Transformer-Based Models in Textual-Based NLP
Rahali, A., & Akhloufi, M. A. (2023) · AI · doi:10.3390/ai4010004 · Cited by 187 · see citing works
Unsupervised Domain Clusters in Pretrained Language Models
Aharoni, R., & Goldberg, Y. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.692 · Cited by 181 · see citing works
Lewis, P., Ott, M., Du, J., & Stoyanov, V. (2020) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2020.clinicalnlp-1.17 · Cited by 175 · see citing works
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Zhang, N., Chen, M., Bi, Z., Liang, X., Li, L., Shang, X., Yin, K., Tan, C., Xu, J., Huang, F., et al. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.544 · Cited by 172 · see citing works
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation
He, R., Liu, L., Ye, H., Tan, Q., Ding, B., Cheng, L., Low, J.-W., Bing, L., & Si, L. (2021) · doi:10.18653/v1/2021.acl-long.172 · Cited by 172 · see citing works
SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge
Ke, P., Ji, H., Liu, S., Zhu, X., & Huang, M. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.567 · Cited by 169 · see citing works
Exploring Wav2vec 2.0 Fine Tuning for Improved Speech Emotion Recognition
Chen, L.-W., & Rudnicky, A. I. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095036 · Cited by 166 · see citing works
Koto, F., Lau, J. H., & Baldwin, T. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.833 · Cited by 162 · see citing works
Investigating Pretrained Language Models for Graph-to-Text Generation
Ribeiro, L. F. R., Schmitt, M., Schütze, H., & Gurevych, I. (2021) · Workshop on NLP for Conversational AI (NLP4ConvAI) · doi:10.18653/v1/2021.nlp4convai-1.20 · Cited by 160 · see citing works
Feng, S., Park, C. Y., Liu, Y., & Tsvetkov, Y. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.656 · Cited by 149 · see citing works
He, K., Mao, R., Lin, Q., Ruan, Y., Lan, X., Feng, M., & Cambria, E. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.102963 · Cited by 147 · see citing works
Word Alignment by Fine-tuning Embeddings on Parallel Corpora
Dou, Z.-Y., & Neubig, G. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.181 · Cited by 147 · see citing works
CrossNER: Evaluating Cross-Domain Named Entity Recognition
Liu, Z., Xu, Y., Yu, T., Dai, W., Ji, Z., Cahyawijaya, S., Madotto, A., & Fung, P. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17587 · Cited by 142 · see citing works
BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model
Yuan, H., Yuan, Z., Gan, R., Zhang, J., Xie, Y., & Yu, S. (2022) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2022.bionlp-1.9 · Cited by 142 · see citing works
A comprehensive survey on machine learning approaches for fake news detection
Alghamdi, J., Luo, S., & Lin, Y. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17470-8 · Cited by 141 · see citing works
LAnoBERT: System log anomaly detection based on BERT masked language model
Lee, Y., Kim, J., & Kang, P. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110689 · Cited by 139 · see citing works
One Embedder, Any Task: Instruction-Finetuned Text Embeddings
Su, H., Shi, W., Kasai, J., Wang, Y., Hu, Y., Mari Ostendorf, E., Yih, W.-T., Smith, N. A., Zettlemoyer, L., & Yu, T. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.71 · Cited by 136 · see citing works
Self-citationMTP: Advancing Remote Sensing Foundation Model via Multitask Pretraining
Wang, D., Zhang, J., Xu, M., Liu, L., Wang, D., Gao, E., Han, C., Guo, H., Du, B., Tao, D., et al. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3408154 · Cited by 136 · see citing works
Transformer models in biomedicine
Madan, S., Lentzen, M., Brandt, J., Rueckert, D., Hofmann‐Apitius, M., & Fröhlich, H. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02600-5 · Cited by 133 · see citing works
A Comparative Study of Machine Learning and Deep Learning Techniques for Fake News Detection
Alghamdi, J., Lin, Y., & Luo, S. (2022) · Information · doi:10.3390/info13120576 · Cited by 129 · see citing works
Learning to Pre-train Graph Neural Networks
Lu, Y., Jiang, X., Fang, Y., & Shi, C. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i5.16552 · Cited by 127 · see citing works
Improving and Simplifying Pattern Exploiting Training
Tam, D., Menon, R. R., Bansal, M., Srivastava, S., & Raffel, C. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.407 · Cited by 124 · see citing works
Towards COVID-19 fake news detection using transformer-based models
Alghamdi, J., Lin, Y., & Luo, S. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110642 · Cited by 124 · see citing works
Systematic review of data-centric approaches in artificial intelligence and machine learning
Singh, P. (2023) · Data Science and Management · doi:10.1016/j.dsm.2023.06.001 · Cited by 124 · see citing works
Merging Models with Fisher-Weighted Averaging
Matena, M. S., & Raffel, C. (2022) · neural information processing systems · doi:10.52202/068431-1287 · Cited by 123 · see citing works
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 123 · see citing works
CLEVE: Contrastive Pre-training for Event Extraction
Wang, Z., Wang, X., Han, X., Lin, Y., Hou, L., Liu, Z., Li, P., Li, J., & Zhou, J. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.491 · Cited by 121 · see citing works
A taxonomy and review of generalization research in NLP
Hupkes, D., Giulianelli, M., Dankers, V., Artetxe, M., Elazar, Y., Pimentel, T., Christodoulopoulos, C., Lasri, K., Saphra, N., Sinclair, A., et al. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00729-y · Cited by 121 · see citing works
Addressing bias in generative AI: Challenges and research opportunities in information management
Wei, X., Kumar, N., & Zhang, H. (2025) · Information & Management · doi:10.1016/j.im.2025.104103 · Cited by 121 · see citing works
DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented DialoguePreprint
Mehri, S., Eric, M., & Hakkani-Tür, D. Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.13570 · Cited by 118 · see citing works
Generative Data Augmentation for Commonsense Reasoning
Yang, Y., Malaviya, C., Fernandez, J., Swayamdipta, S., Bras, R. J. L., Wang, J., Bhagavatula, C., Choi, Y., & Downey, D. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.90 · Cited by 117 · see citing works
Self-citationTowards Geospatial Foundation Models via Continual Pretraining
Mendieta, M., Han, B., Shi, X., Zhu, Y., & Chen, C. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01541 · Cited by 117 · see citing works
Challenges in Detoxifying Language Models
Welbl, J., Glaese, A., Uesato, J., Dathathri, S., Mellor, J., Hendricks, L. A., Anderson, K., Kohli, P., Coppin, B., & Huang, P.-S. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.210 · Cited by 112 · see citing works
LogFiT: Log Anomaly Detection Using Fine-Tuned Language Models
Almodovar, C., Sabrina, F., Karimi, S., & Azad, S. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3358730 · Cited by 111 · see citing works
LAMBERT: Layout-Aware Language Modeling for Information Extraction
Garncarek, Ł., Powalski, R., Stanisławek, T., Topolski, B., Halama, P., Turski, M., & Graliński, F. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86549-8_34 · Cited by 108 · see citing works
CausaLM: Causal Model Explanation Through Counterfactual Language Models
Feder, A., Oved, N., Shalit, U., & Reichart, R. (2021) · Computational Linguistics · doi:10.1162/coli_a_00404 · Cited by 105 · see citing works
Self-Supervised Pretraining Improves Self-Supervised Pretraining
Reed, C., Yue, X., Nrusimha, A., Ebrahimi, S., Vijaykumar, V., Mao, R., Li, B., Zhang, S., Guillory, D., Metzger, S. L., et al. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00112 · Cited by 105 · see citing works
Rethinking embedding coupling in pre-trained language modelsPreprint
Chung, H. W., Févry, T., Tsai, H., Johnson, M., & Ruder, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.12821 · Cited by 102 · see citing works
Patwa, P., Bhardwaj, M., Guptha, V., Kumari, G., Sharma, S., Pykl, S., Das, A., Ekbal, A., Akhtar, M. S., & Chakraborty, T. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-73696-5_5 · Cited by 101 · see citing works
CodeBERTScore: Evaluating Code Generation with Pretrained Models of Code
Zhou, S., Alon, U., Agarwal, S., & Neubig, G. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.859 · Cited by 100 · see citing works
Improved Text Classification via Contrastive Adversarial Training
Pan, L., Hang, C.-W., Sil, A., & Potdar, S. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21362 · Cited by 99 · see citing works
Yu, Y., Zuo, S., Jiang, H., Ren, W., Zhao, T., & Zhang, C. (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.84 · Cited by 98 · see citing works
PLM-ICD: Automatic ICD Coding with Pretrained Language Models
Huang, C.-W., Tsai, S.-C., & Chen, Y.-N. (2022) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2022.clinicalnlp-1.2 · Cited by 98 · see citing works
Mining legal arguments in court decisions
Habernal, I., Faber, D. R., Recchia, N., Bretthauer, S., Gurevych, I., Döhmann, I. S. G., & Burchard, C. (2023) · Artificial Intelligence and Law · doi:10.1007/s10506-023-09361-y · Cited by 97 · see citing works
Application of large language models in medicine
Liu, F., Zhou, H., Gu, B., Zou, X., Huang, J., Wu, J., Li, Y., Chen, S. S., Hua, Y., Zhou, P., et al. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00279-5 · Cited by 94 · see citing works
ExT5: Towards Extreme Multi-Task Scaling for Transfer LearningPreprint
Aribandi, V., Tay, Y., Schuster, T., Rao, J., Zheng, H., Mehta, S. V., Zhuang, H., Tran, V. Q., Bahri, D., Ni, J., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.10952 · Cited by 94 · see citing works
Understanding tables with intermediate pre-training
Eisenschlos, J. M., Krichene, S., & Müller, T. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.27 · Cited by 92 · see citing works
- Authors
- Gururangan, S., Marasović, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A.
- Affiliations
- Allen Institute for Artificial Intelligence (US)
- Venue
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Published
- 2020
- Pages
- 8342-8360
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W3017961061
- Cited by
- 2,103
- References
- 62
- Citations by year
- 2026: 246 · 2025: 313 · 2024: 253 · 2023: 443 · 2022: 331 · 2021: 403 · 2020: 111 · 2019: 3
- FWCI
- 122.98
- 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)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- domain-adaptive pretraining, language model fine-tuning, text classification, resource-limited settings, data selection, biomedical text, computer science literature
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 ›
Are you an author, or do you see a problem with this page? Request a review
