The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Self-Instruct: Aligning Language Models with Self-Generated Instructions
Open accessAbstract
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, Hannaneh Hajishirzi. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
Overview
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
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
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
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208
Training Language Models to Follow Instructions with Human Feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-2011 · Cited by 3,117
The Power of Scale for Parameter-Efficient Prompt Tuning
Lester, B., Al-Rfou, R., & Constant, N. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.243 · Cited by 3,096
The Power of Scale for Parameter-Efficient Prompt Tuning
Lester, B., Al-Rfou, R., & Constant, N. (2021) · arXiv · doi:10.48550/arxiv.2104.08691 · Cited by 3,092
Self-Training With Noisy Student Improves ImageNet Classification
Xie, Q., Luong, M.-T., Hovy, E., & Le, Q. V. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01070 · Cited by 2,393
Scaling Instruction-Finetuned Language Models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2210.11416 · Cited by 1,441
Finetuned Language Models Are Zero-Shot Learners
Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., & Le, Q. V. (2021) · arXiv · doi:10.48550/arxiv.2109.01652 · Cited by 1,101
A Survey of Data Augmentation Approaches for NLP
Feng, S. Y., Gangal, V., Wei, J., Chandar, S., Vosoughi, S., Mitamura, T., & Hovy, E. H. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.84 · Cited by 972
ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks
Shridhar, M., Thomason, J., Gordon, D., Bisk, Y., Han, W., Mottaghi, R., Zettlemoyer, L., & Fox, D. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01075 · Cited by 540
Constituency Parsing with a Self-Attentive Encoder
Kitaev, N., & Klein, D. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1249 · Cited by 531
Unnatural Instructions: Tuning Language Models with (Almost) No Human LaborPreprint
Honovich, O., Scialom, T., Levy, O., & Schick, T. (2022) · Annual Meeting of the Association for Computational Linguistics · doi:10.48550/arxiv.2212.09689 · Cited by 516
Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data
Xu, C., Guo, D., Duan, N., & McAuley, J. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.385 · Cited by 348
Large Language Models Are Human-Level Prompt Engineers
Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H., & Ba, J. (2022) · arXiv · doi:10.48550/arxiv.2211.01910 · Cited by 330
Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor
Honovich, O., Scialom, T., Levy, O., & Schick, T. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.806 · Cited by 324
Speaker-Follower Models for Vision-and-Language Navigation
Fried, D., Hu, R., Cirik, V., Rohrbach, A., Andreas, J., Morency, L.-P., Berg-Kirkpatrick, T., Saenko, K., Klein, D., & Darrell, T. (2018) · arXiv · doi:10.48550/arxiv.1806.02724 · Cited by 291
Cross-Task Generalization via Natural Language Crowdsourcing Instructions
Mishra, S., Khashabi, D., Baral, C., & Hajishirzi, H. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.244 · Cited by 269
Self-citationSuper-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Wang, Y., Mishra, S., Alipoormolabashi, P., Kordi, Y., Mirzaei, A., Naik, A., Ashok, A., Dhanasekaran, A. S., Arunkumar, A., Stap, D., et al. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.340 · Cited by 247
Self-citationAmini, M.-R., Feofanov, V., Pauletto, L., Hadjadj, L., Devijver, É., & Maximov, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128904 · Cited by 244
Large Language Models Can Self-Improve
Huang, J., Gu, S. S., Hou, L., Wu, Y., Wang, X., Yu, H., & Han, J. (2022) · arXiv · doi:10.48550/arxiv.2210.11610 · Cited by 230
Large Language Models Can Self-Improve
Huang, J., Gu, S., Hou, L., Wu, Y., Wang, X., Yu, H., & Han, J. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.67 · Cited by 230
Multilingual Constituency Parsing with Self-Attention and Pre-Training
Kitaev, N., Cao, S., & Klein, D. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1340 · Cited by 228
Symbolic Knowledge Distillation: from General Language Models to Commonsense Models
West, P., Bhagavatula, C., Hessel, J., Hwang, J. D., Jiang, L., Bras, R. J. L., Lu, X., Welleck, S., & Choi, Y. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.341 · Cited by 209
WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation
Liu, A., Swayamdipta, S., Smith, N. A., & Choi, Y. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.508 · Cited by 178
Self-citationPromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
Bach, S., Sanh, V., Yong, Z. X., Webson, A., Raffel, C., Nayak, N. V., Sharma, A., Kim, T., Bari, M. S., Fevry, T., et al. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.18653/v1/2022.acl-demo.9 · Cited by 178
Instruction Induction: From Few Examples to Natural Language Task Descriptions
Honovich, O., Shaham, U., Bowman, S. R., & Levy, O. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.108 · Cited by 160
Self-training Improves Pre-training for Natural Language Understanding
Du, J., Grave, É., Gunel, B., Chaudhary, V., Çelebi, O., Auli, M., Stoyanov, V., & Conneau, A. (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.426 · Cited by 159
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 156
Revisiting Self-Training for Neural Sequence GenerationPreprint
He, J., Gu, J., Shen, J., & Ranzato, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.13788 · Cited by 150
Large Language Models Struggle to Learn Long-Tail Knowledge
Kandpal, N., Deng, H., Roberts, A., Wallace, E., & Raffel, C. (2022) · arXiv · doi:10.48550/arxiv.2211.08411 · Cited by 143
Teaching Small Language Models to Reason
Magister, L. C., Mallinson, J., Adamek, J., Malmi, E., & Severyn, A. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/2023.acl-short.151 · Cited by 127
Generating Datasets with Pretrained Language Models
Schick, T., & Schütze, H. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.555 · Cited by 122
Towards Zero-Label Language LearningPreprint
Wang, Z.-R., Yu, A. W., Fırat, O., & Cao, Y. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.09193 · Cited by 94
FILM: Following Instructions in Language with Modular Methods
Min, S. Y., Chaplot, D. S., Ravikumar, P., Bisk, Y., & Salakhutdinov, R. (2021) · arXiv · doi:10.48550/arxiv.2110.07342 · Cited by 81
Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision
Sun, Z., Shen, Y., Zhou, Q., Zhang, H., Chen, Z., Cox, D., Yang, Y., & Gan, C. (2023) · arXiv · doi:10.48550/arxiv.2305.03047 · Cited by 81
Prompt Consistency for Zero-Shot Task Generalization
Zhou, C., He, J., Ma, X., Berg-Kirkpatrick, T., & Neubig, G. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.192 · Cited by 68
Learning from Task Descriptions
Weller, O., Lourie, N., Gardner, M., & Peters, M. E. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.105 · Cited by 67
Impact of Pretraining Term Frequencies on Few-Shot Reasoning
Razeghi, Y., Logan, R. L., Gardner, M., & Singh, S. (2022) · arXiv · doi:10.48550/arxiv.2202.07206 · Cited by 53
Pre-trained Language Models Can be Fully Zero-Shot Learners
Zhao, X., Ouyang, S., Yu, Z.-G., Wu, M.-L., & Li, L. (2022) · Annual Meeting of the Association for Computational Linguistics · doi:10.48550/arxiv.2212.06950 · Cited by 45
Generating Sequences by Learning to Self-Correct
Welleck, S., Lu, X., West, P., Brahman, F., Shen, T., Khashabi, D., & Choi, Y. (2022) · arXiv · doi:10.48550/arxiv.2211.00053 · Cited by 41
Self-citationExplaining Patterns in Data with Language Models via Interpretable AutopromptingPreprint
Singh, C., Morris, J. X., Aneja, J., Rush, A. M., & Gao, J. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2210.01848 · Cited by 37
Pre-trained Language Models Can be Fully Zero-Shot Learners
Zhao, X., Ouyang, S., Yu, Z., Wu, M., & Li, L. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.869 · Cited by 36
Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot LearningPreprint
Yu, M., Michalski, M., Huang, J., Zhang, Y., Abdelzaher, T., & Han, J. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2211.03044 · Cited by 32
Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot LearnersPreprint
Ye, S., Kim, D., Jang, J., Shin, J., & Seo, M. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2210.02969 · Cited by 24
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 10
Teaching Small Language Models to Reason
Magister, L. C., Mallinson, J., Adamek, J., Malmi, E., & Severyn, A. (2022) · arXiv · doi:10.48550/arxiv.2212.08410 · Cited by 7
Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat DataPreprint
Xu, C., Guo, D., Duan, N., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2304.01196 · Cited by 7
Instruction Induction: From Few Examples to Natural Language Task DescriptionsPreprint
Honovich, O., Shaham, U., Bowman, S. R., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2205.10782 · Cited by 6
One-Shot Learning from a Demonstration with Hierarchical Latent Language
Weir, N., Yuan, X., Côté, M.-A., Hausknecht, M., Laroche, R., Momennejad, I., van Seijen, H., & Durme, B. V. (2023) · Adaptive Agents and Multi-Agents Systems · doi:10.65109/kagr5160 · Cited by 4
One-Shot Learning from a Demonstration with Hierarchical Latent LanguagePreprint
Weir, N., Yuan, X., Côté, M.-A., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2203.04806 · Cited by 2
Amini, M.-R., Feofanov, V., Pauletto, L., Hadjadj, L., Devijver, E., & Maximov, Y. (2022) · arXiv · doi:10.48550/arxiv.2202.12040
Leveraging QA Datasets to Improve Generative Data Augmentation
Mekala, D., Vu, T., Schick, T., & Shang, J. (2022) · arXiv · doi:10.48550/arxiv.2205.12604
Research works printed but not linked: 3
- Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. In Advances in Neural Information Processing Systems (NeurIPS) Workshop on Energy Efficient Machine Learning and Cognitive Computing.
- Victor Sanh, Albert Webson, Colin Raffel, Stephen Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Tr- ishala Neeraj, Jos Rozen, Abheesht Sharma, An- drea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Teven Le Scao, Stella Biderman, Leo Gao, Thomas Wolf, and Alexander M Rush. 2022. Multi- task Prompted Training Enables Zero-Shot Task Gen- eralization. In International Conference on Learning Representations (ICLR).
- Eric Zelikman, Jesse Mu, Noah D Goodman, and Yuhuai Tony Wu. 2022. STar: Self-taught reasoner bootstrapping reasoning with reasoning. In Advances in Neural Information Processing Systems (NeurIPS).
Cited by
Sorted by citations
QLoRA: Efficient Finetuning of Quantized LLMs
Dettmers, T., Pagnoni, A., Holtzman, A., & Zettlemoyer, L. (2023) · arXiv · doi:10.48550/arxiv.2305.14314 · Cited by 3,832
Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E. P., et al. (2023) · arXiv · doi:10.48550/arxiv.2306.05685 · Cited by 1,888
Efficient Memory Management for Large Language Model Serving with PagedAttention
Kwon, W., Li, Z., Zhuang, S., Sheng, Y., Zheng, L., Yu, C. H., Gonzalez, J., Zhang, H., & Stoica, I. (2023) · Proceedings of the 29th Symposium on Operating Systems Principles · doi:10.1145/3600006.3613165 · Cited by 1,817
A Survey of Large Language Models
Zhao, W. X., Zhou, K., Li, J., Tang, T., Dong, Z., Hou, Y., Zhang, B., Min, Y., Zhang, J., Liu, P., et al. (2026) · Frontiers of Computer Science · doi:10.1007/s11704-026-60308-3 · Cited by 2,093
A Comprehensive Overview of Large Language Models
Naveed, H., Khan, A. U., Qiu, S., Saqib, M., Anwar, S., Usman, M., Akhtar, N., Barnes, N., & Mian, A. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3744746 · Cited by 1,257
Metadata
- Authors
- Wang, Y., Kordi, Y., Mishra, S., Liu, A., Smith, N. A., Khashabi, D., & Hajishirzi, H.
- Affiliations
- Johns Hopkins University (US)
- Corresponding author
- Wang, Yizhong
- Venue
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2023
- Pages
- 13484-13508
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4385572634
- Cited by
- 1,243
- References
- 39
- Citations by year
- 2026: 317 · 2025: 435 · 2024: 345 · 2023: 145
- FWCI
- 196.73
- 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)
- Funders
- Defense Advanced Research Projects Agency (N66001-19-2-4031, N66001-19)
- Multidisciplinary University Research Initiative
- Office of Naval Research (N00014‐18‐1‐2670, N00014-18-1-2826, MURI N00014-18-1-2670, N00014)
- Naval Information Warfare Center Pacific
- 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
- self-education, language model alignment, instruction tuning, self-supervised learning, large language models
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