Generating Datasets with Pretrained Language Models
Open accessAbstract
To obtain high-quality sentence embeddings from pretrained language models (PLMs), they must either be augmented with additional pretraining objectives or finetuned on a large set of labeled text pairs.While the latter approach typically outperforms the former, it requires great human effort to generate suitable datasets of sufficient size.In this paper, we show how PLMs can be leveraged to obtain high-quality sentence embeddings without the need for labeled data, finetuning or modifications to the pretraining objective: We utilize the generative abilities of large and high-performing PLMs to generate entire datasets of labeled text pairs from scratch, which we then use for finetuning much smaller and more efficient models.Our fully unsupervised approach outperforms strong baselines on several semantic textual similarity datasets.1
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Pre-trained LMs can generate datasets given certain instructions.
“Schick and Schütze [119] propose to use pre-trained LMs to generate datasets given certain instructions.”
From Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (2023) · cites this paper as [119]
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“Data generation has seen advances in either simplifying or fully automating the creation of questions and labels [Par+22; Liu+22; SS21; Wan+21].”
MethodThe Role of Federated Learning in a Wireless World with Foundation Models
Chen, Z., Yang, H. H., Tay, Y. C., Chong, K. F. E., & Quek, T. Q. S. (2024) · IEEE Wireless Communications · doi:10.1109/mwc.005.2300481 · Cited by 28 · see citing works
ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback
Ye, J., Gao, J., Wu, Z., Feng, J., Yu, C., & Kong, L. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.269 · Cited by 37 · see citing works
Generating Better Items for Cognitive Assessments Using Large Language Models
Laverghetta, A., & Licato, J. (2023) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/2023.bea-1.34 · Cited by 27 · see citing works
STraTA: Self-Training with Task Augmentation for Better Few-shot Learning
Vu, T., Luong, M.-T., Le, Q. V., Simon, G., & Iyyer, M. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.462 · Cited by 30 · see citing works
Improving Contrastive Learning of Sentence Embeddings from AI Feedback
Cheng, Q., Yang, X., Sun, T., Li, L., & Qiu, X. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.707 · Cited by 28 · see citing works
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ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval
Yu, Y., Zhuang, Y., Zhang, R., Yu, M., Shen, J., & Zhang, C. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.748 · Cited by 26 · see citing works
Explainable AI: XAI-guided context-aware data augmentation
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Differentially Private Language Models for Secure Data Sharing
Mattern, J., Jin, Z., Weggenmann, B., Schölkopf, B., & Sachan, M. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.323 · Cited by 24 · see citing works
UniHD at TSAR-2022 Shared Task: Is Compute All We Need for Lexical Simplification?
Aumiller, D., & Gertz, M. (2022) · Workshop on Text Simplification, Accessibility and Readability (TSAR) · doi:10.18653/v1/2022.tsar-1.28 · Cited by 15 · see citing works
Synthetically generated text for supervised text analysis
Halterman, A. (2025) · Political Analysis · doi:10.1017/pan.2024.31 · Cited by 17 · see citing works
A Robust Bias Mitigation Procedure Based on the Stereotype Content Model
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Text Data Augmentation for the Korean Language
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Don’t Prompt, Search! Mining-based Zero-Shot Learning with Language Models
van de Kar, M., Xia, M., Chen, D., & Artetxe, M. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.509 · Cited by 13 · see citing works
Contrastive Learning of Sentence Embeddings from Scratch
Zhang, J., Lan, Z., & He, J. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.238 · Cited by 22 · see citing works
Geographic and Geopolitical Biases of Language Models
Faisal, F., & Anastasopoulos, A. (2023) · Workshop on Multilingual Representation Learning (MRL) · doi:10.18653/v1/2023.mrl-1.12 · Cited by 13 · see citing works
Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework
Chen, Y., Zhang, Y., Wang, B., Liu, Z., & Li, H. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.558 · Cited by 12 · see citing works
Data Augmentation for Low-Resource Dialogue Summarization
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Jin, Z., Wang, X., Cheng, F., Sun, C., Liu, Q., & Qu, H. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3236380 · Cited by 12 · see citing works
Li, H., Wu, Y., Schlegel, V., Batista-Navarro, R., Nguyen, T.-T., Kashyap, A. R., Zeng, X., Beck, D., WINKLER, S. S., & Nenadić, G. (2023) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2023.bionlp-1.49 · Cited by 11 · see citing works
Hybrid Data Augmentation for Citation Function Classification
Zhang, Y., Wang, Y., Sheng, Q. Z., Mahmood, A., Zhang, W. E., & Zhao, R. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191695 · Cited by 10 · see citing works
Math Word Problem Solving by Generating Linguistic Variants of Problem Statements
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VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Models
Li, S., Li, L., Liu, Y., Ren, S., Liu, Y., Gao, R., Sun, X., & Hou, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72897-6_19 · Cited by 10 · see citing works
Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues
Ou, J., Zhang, J., Feng, Y., & Zhou, J. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.106 · Cited by 12 · see citing works
Sclar, M., West, P., Kumar, S., Tsvetkov, Y., & Choi, Y. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.655 · Cited by 10 · see citing works
Unsupervised Neural Machine Translation with Generative Language Models OnlyPreprint
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Liu, H., Zhao, S., Zhang, X., Zhang, F., Wang, W. W., Ma, F., Chen, H., Hong, Y., & Zhang, X. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29827 · Cited by 10 · see citing works
Towards Generating High-Quality Knowledge Graphs by Leveraging Large Language Models
Ezzabady, M. K., Ieng, F., Khorashadizadeh, H., Benamara, F., Groppe, S., & Sahri, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70239-6_31 · Cited by 9 · see citing works
Delmas, M., Wysocka, M., & Freitas, A. (2024) · Computational Linguistics · doi:10.1162/coli_a_00520 · Cited by 10 · see citing works
Seinen, T. M., Kors, J. A., van Mulligen, E. M., & Rijnbeek, P. R. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae159 · Cited by 8 · see citing works
Italiani, P., Moro, G., & Ragazzi, L. (2025) · Artificial Intelligence and Law · doi:10.1007/s10506-025-09463-9 · Cited by 10 · see citing works
Yuan, M., Kao, B., Wu, T.-H., Cheung, M. M. K., Chan, H. W. H., Cheung, A. S. Y., Chan, F. W. H., & Chen, Y. (2023) · Artificial Intelligence and Law · doi:10.1007/s10506-023-09367-6 · Cited by 8 · see citing works
Leveraging QA Datasets to Improve Generative Data Augmentation
Mekala, D., Vu, T., Schick, T., & Shang, J. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.660 · Cited by 8 · see citing works
Self-citationAddressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation
Yang, K., Deng, O., Chen, C., Shin, R., Roy, S., & Durme, B. V. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.291 · Cited by 8 · see citing works
Utilizing Large Language Models to Synthesize Product Desirability Datasets
Hastings, J. D., Weitl-Harms, S., Doty, J., Myers, Z. J., & Thompson, W. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10826001 · Cited by 7 · see citing works
Knowledge-Grounded Conversational Data Augmentation with Generative Conversational Networks
Lin, Y. T., Papangelis, A., Kim, S., & Hakkani-Tür, D. Z. (2022) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2022.sigdial-1.3 · Cited by 7 · see citing works
Universal Self-Adaptive Prompting
Wan, X., Sun, R., Nakhost, H., Dai, H., Eisenschlos, J. M., Arık, S. Ö., & Pfister, T. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.461 · Cited by 11 · see citing works
Chen, Y., Xi, N., Du, Y., Wang, H., Chen, J., Zhao, S., & Qin, B. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i20.30198 · Cited by 8 · see citing works
A Logical Fallacy-Informed Framework for Argument Generation
Mouchel, L., Paul, D., Cui, S., West, R., Bosselut, A., & Faltings, B. (2025) · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2025.naacl-long.374 · Cited by 7 · see citing works
Scroggins, J. K., Topaz, M., Song, J., & Zolnoori, M. (2024) · Journal of Nursing Scholarship · doi:10.1111/jnu.13004 · Cited by 6 · see citing works
MotionGPT: Human Motion Synthesis with Improved Diversity and Realism via GPT-3 Prompting
Ribeiro-Gomes, J., Cai, T., Milacski, Z. Á., Wu, C. H., Prakash, A., Takagi, S., Aubel, A., Kim, D., Bernardino, A. J. M., & la Torre, F. D. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00499 · Cited by 6 · see citing works
ZeroAE: Pre-trained Language Model based Autoencoder for Transductive Zero-shot Text Classification
Guo, K., Yu, H., Liao, C., Li, J., & Zhang, H. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.200 · Cited by 6 · see citing works
What happens before and after: Multi-Event Commonsense in Event Coreference Resolution
Ravi, S., Tanner, C., Ng, R. T., & Shwartz, V. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.125 · Cited by 6 · see citing works
Data Augmentation with Large Language Models for Vietnamese Abstractive Text Summarization
Le, H. M., Luong, V. T., & Luong, N. H. (2023) · International Conference on Multimedia Analysis and Pattern Recognition (MAPR) · doi:10.1109/mapr59823.2023.10288906 · Cited by 5 · see citing works
Leveraging Large Language Models for Structure Learning in Prompted Weak Supervision
Su, J., Yu, P., Zhang, J., & Bach, S. H. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386190 · Cited by 5 · see citing works
Learning Evaluation Models From Large Language Models for Sequence Generation
Wang, C., Zhou, H., Chang, K., Liu, T., Zhang, C., Yang, M., Du, Q., Xiao, T., Zhang, Y., & Zhu, J. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3587460 · Cited by 7 · see citing works
Li, H., Schlegel, V., Batista-Navarro, R., & Nenadić, G. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.786 · Cited by 5 · see citing works
Alfred: A System for Prompted Weak Supervision
Yu, P., & Bach, S. H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-demo.46 · Cited by 5 · see citing works
Pneg: Prompt-based Negative Response Generation for Dialogue Response Selection Task
Lee, N., Park, C., Choi, H.-J., & Choo, J. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.733 · Cited by 4 · see citing works
Kim, B., Seo, S., Han, S., Erdenee, E., & Chang, B. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.286 · Cited by 5 · see citing works
An Empirical Analysis of Leveraging Knowledge for Low-Resource Task-Oriented Semantic Parsing
Kulkarni, M., Zhong, A., Mesnards, N. G. D., Movaghati, S., Sridhar, M., Xie, H., & Lu, J. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.123 · Cited by 3 · see citing works
Reliable Data Generation and Selection for Low-Resource Relation Extraction
Yu, J., Wang, X., & Chen, W. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29915 · Cited by 4 · see citing works
Li, M., Zhang, R., Nie, Z., & Mao, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i12.29263 · Cited by 3 · see citing works
AuToGen: Automated Tool Learning Data Generation with Domain-specific Structured Data
Zeng, D., Zhou, L., Zhang, Z., & Jiang, L. (2024) · Data Intelligence · doi:10.3724/2096-7004.di.2024.0005 · Cited by 3 · see citing works
WikiOmnia: filtration and evaluation of the generated QA corpus on the whole Russian Wikipedia
Pisarevskaya, D., & Shavrina, T. (2022) · Workshop on Generation, Evaluation and Metrics (GEM) · doi:10.18653/v1/2022.gem-1.10 · Cited by 3 · see citing works
A Quantum Language-Inspired Tree Structural Text Representation for Semantic Analysis
Yan, Y., Qiu, D., & Yan, R. (2022) · Mathematics · doi:10.3390/math10060914 · Cited by 2 · see citing works
Collective Human Opinions in Semantic Textual Similarity
Wang, Y., Tao, S., Xie, N., Yang, H., Baldwin, T., & Verspoor, K. M. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00584 · Cited by 3 · see citing works
Alshamrani, S. (2024) · IEEE Access · doi:10.1109/access.2024.3434612 · Cited by 2 · see citing works
SumREN: Summarizing Reported Speech about Events in News
Reddy, R. G., Elfardy, H., Chan, H. P., Small, K., & Ji, H. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i11.26506 · Cited by 2 · see citing works
BARLE: Background-Aware Representation Learning for Background Shift Out-of-Distribution Detection
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Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs
Golde, J., Haller, P., Hamborg, F., Risch, J., & Akbik, A. (2023) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2023.emnlp-demo.1 · Cited by 2 · see citing works
Data Augmentation with GPT-3.5 for Vietnamese Natural Language Inference
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Unveiling the Evolution and Impact of Pretrained Language Models in Natural Language Processing
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Large Models are Good Annotators for Zero-Shot Learning
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Predicting organized collective violence through hostile discourse in social media
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CHEF in the Language Kitchen: A Generative Data Augmentation Leveraging Korean Morpheme Ingredients
Seo, J., Moon, H., Lee, J., Eo, S., Park, C., & Lim, H. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.367 · Cited by 1 · see citing works
A Simple Method to Improve the Performance of Small Pre-trained Language Models on Few-shot Tasks
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- Authors
- Schick, T., & Schütze, H.
- Affiliations
- Ludwig-Maximilians-Universität München (DE)
- ORCID
- Schütze, Hinrich
- Venue
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Published
- 2021
- Pages
- 6943-6951
- OpenAlex
- W3154863804
- Cited by
- 122
- References
- 47
- Citations by year
- 2026: 7 · 2025: 15 · 2024: 27 · 2023: 39 · 2022: 26 · 2021: 8
- FWCI
- 11.55
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Universitätsbibliothek der LMU
- Funders
- H2020 European Research Council (740516)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- sentence embeddings, pre-trained language models, semantic textual similarity, generative language models, fine-tuning
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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