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Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
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Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Atharva Naik, Arjun Ashok, Arut Selvan Dhanasekaran, Anjana Arunkumar, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Kuntal Kumar Pal, Maitreya Patel, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Savan Doshi, Shailaja Keyur Sampat, Siddhartha Mishra, Sujan Reddy A, Sumanta Patro, Tanay Dixit, Xudong Shen. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.
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“While LLMs have been shown to excel at these tasks (Wang et al., 2022;Zhong et al., 2023)”
From MedScore: Generalizable Factuality Evaluation of Open-ended Long-form Medical Answers by Domain-adapted Claim Decomposition and Verification (2026) · cites this paper as Wang et al., 2022
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In the citing paper
“Our grammar is directly tested by verifying that it can generate the formatting associated with 100+ Super-NaturalInstructions tasks (Wang et al., 2022).”
From Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting (2023) · cites this paper as Wang et al., 2022
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In the citing paper
“Alpaca (Taori et al., 2023) is based on LLAMA (Touvron et al., 2023) fine-tuned on the instructions data based on InstructGPT following the recipe from Wang et al. (2022).”
From FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation (2023) · cites this paper as Wang et al., 2022
Experimental — accuracy not yet measuredIn the citing paper
“The current limited number of tasks may explain why we have not yet observed the models exhibiting emergent abilities to generalize to unseen clinical tasks, a capability seen in LLMs trained on thousands of diverse tasks in the general domain17,22.”
From Towards evaluating and building versatile large language models for medicine (2025) · cites this paper as [17]
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Litschko, R., Müller-Eberstein, M., van der Goot, R., Weber, L., & Plank, B. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.14 · Cited by 7
Improving optimal prompt learning through multilayer fusion and latent dirichlet allocation
Chen, Q., Korneder, J., Rawashdeh, O. A., Wang, Y., & Louie, W.-Y. G. (2025) · Frontiers in Robotics and AI · doi:10.3389/frobt.2025.1579990 · Cited by 3
Self-citationMURI: High-Quality Instruction Tuning Datasets for Low-Resource Languages via Reverse Instructions
Köksal, A., Thaler, M., Imani, A., Üstün, A., Korhonen, A., & Schütze, H. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.18 · Cited by 16
Huang, H., DeLucia, A., Tiyyala, V. M., & Dredze, M. (2026) · Findings of the Association for Computational Linguistics: ACL 2026 · doi:10.18653/v1/2026.findings-acl.693 · Cited by 2
“Previous factuality-related work (Min et al., 2023; Wanner et al., 2025) mostly use Inst-LLAMA (Touvron et al., 2023; Wang et al., 2022) as 1.”
InfluentialGenerating an Instruction Dataset to Build Cyber Intelligent Large Language Models
Zaleppa, P., Kaza, S., & Taylor, B. (2024) · Cyber Awareness and Research Symposium (CARS) · doi:10.1109/cars61786.2024.10778717 · Cited by 3
Cross-Domain Tibetan Named Entity Recognition via Large Language Models
Zhang, J., Gao, F., Yeshi, L., Tashi, D., Wang, X., Tashi, N., & Luosang, G. (2024) · Electronics · doi:10.3390/electronics14010111 · Cited by 2
MIRA : A Method of Federated Multi-Task Learning for Large Language Models
Elbakary, A., Issaid, C. B., ElBatt, T., Seddik, K. G., & Bennis, M. (2025) · IEEE Networking Letters · doi:10.1109/lnet.2025.3539810 · Cited by 2
AcaQAS: An Academic Question Answering System Based on Finetuning Large Language Models
Lê, T. H., & Duong, B. T. (2025) · SN Computer Science · doi:10.1007/s42979-025-04213-9 · Cited by 2
Systematic survey of various prompt optimization methods and their classifications
Jain, A. M., & Jindal, M. (2025) · International Conference on Computing and Artificial Intelligence (ICCAI) · doi:10.1109/iccai66501.2025.00085 · Cited by 3
Cascading Adversarial Bias from Injection to Distillation in Language Models
Chaudhari, H., Hayes, J., Jagielski, M., Shumailov, I., Nasr, M., & Oprea, A. (2025) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3719027.3765122 · Cited by 2
Route Optimization Reimagined: Multi-Modal Large Language Models for Next-Generation Vehicle Routing
Albalkhi, S. Y., Alotaibi, D. F., Dimitriou, T. D., & Ahmad, I. (2026) · IEEE Access · doi:10.1109/access.2026.3663141 · Cited by 2
EditCoT: A Stepwise Chain-of-Thought Reasoning Framework for Multi-Intent Text Revision
Li, X., Wei, C., & Chen, W. (2026) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3797483 · Cited by 3
España-Bonet, C. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.787 · Cited by 3
Chinese Stylistic Competence: Evaluation Method and Datasets of Large Language Model's Performance
Zhou, L., & Rao, G.-Q. (2023) · International Conference on Asian Language Processing · doi:10.1109/ialp61005.2023.10337306 · Cited by 2
Evaluation of Medium-Sized Language Models in German and English Language
Peinl, R., & Wirth, J. (2024) · International Journal on Natural Language Computing · doi:10.5121/ijnlc.2024.13101 · Cited by 2
Prompt-Based End-to-End Cross-Domain Dialogue State Tracking
Lu, H., Zhong, L., Jiang, H., Chen, W., Yuan, C., & Wang, X. (2024) · Electronics · doi:10.3390/electronics13183587 · Cited by 2
MedQAS: A Medical Question Answering System Based on Finetuning Large Language Models
Duong, B. T., & Lê, T. H. (2024) · Communications in computer and information science · doi:10.1007/978-981-96-0434-0_21 · Cited by 2
Set-Word Embeddings and Semantic Indices: A New Contextual Model for Empirical Language Analysis
de Córdoba, P. F., Pérez, C. A. R., Arnau, C. S., & Sánchez-Pérez, E. A. (2025) · Computers · doi:10.3390/computers14010030 · Cited by 3
Learning modular language-conditioned robot policies through attention
Zhou, Y., Sonawani, S. D., Phielipp, M., Amor, H. B., & Stepputtis, S. (2023) · Autonomous Robots · doi:10.1007/s10514-023-10129-1 · Cited by 10
Whether Current Large Language Models is Suitable for Multimodal Aspect-based Sentiment Analysis?
Song, S., & Zhao, S. (2024) · International Conference on Advances in Artificial Intelligence and Applications (AAIA) · doi:10.1145/3712623.3712644 · Cited by 1
Zhu, S., Rzeszotarski, J. M., & Mimno, D. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3720285 · Cited by 3
Toward Generating Quality Test Questions and Answers Using Quantized Low-Rank Adapters in LLMs
Choi, J., Hong, S., Hong, S., Park, J., & Jung, E.-S. (2025) · IEEE Access · doi:10.1109/access.2025.3570567 · Cited by 2
Active Instruction Tuning for Large Language Models with Reference-Free Instruction Selection
Zhang, Q., Chen, J., & Ma, C. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-8180-8_2 · Cited by 1
Pragmatic inference and mapping for conversational implicature
Hu, J., Liu, Y., Ma, L., Zhang, W.-N., & Liu, T. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02741-7 · Cited by 2
Kaushik, P., Vaidya, A., Chaudhari, S., & Yuille, A. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00070 · Cited by 1
TACOS: Open Tagging and Comparative Scoring for Instruction Fine-Tuning Data Selection
He, X., Yu, H., Sun, Q., Cheng, A., Zhang, T., Liu, C., & Guo, S. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209076 · Cited by 1
Enabling doctor-centric medical AI with LLMs through workflow-aligned tasks and benchmarks
Xie, W., Xiao, Q., Zheng, Y., Wang, X., Chen, J., Ji, K., Gao, A., Tiwari, P., Wan, X., Jiang, F., et al. (2025) · npj Health Systems · doi:10.1038/s44401-025-00038-z · Cited by 1
On the Effect of Instruction Tuning Loss on Generalization
Chatterjee, A., Renduchintala, H. S. V. N. S. K., Bhatia, S., & Chakraborty, T. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.42 · Cited by 1
EMRDCM : An experience-enhanced multi-role debate method based on compromise mechanisms
Zhang, Y., Feng, J., Yiming, X., Wang, Z., Yang, T., & Wang, X. (2026) · Journal of Intelligent Information Systems · doi:10.1007/s10844-026-01022-1 · Cited by 1
Aiyappa, R., Senthilmani, S., An, J., Kwak, H., & Ahn, Y. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3540 · Cited by 14
Metadata
- Authors
- Wang, Y., Mishra, S., Alipoormolabashi, P., Kordi, Y., Mirzaei, A., Naik, A., Ashok, A., Dhanasekaran, A. S., Arunkumar, A., Stap, D., Pathak, E., Karamanolakis, G., Lai, H., Purohit, I., Mondal, I., Anderson, J., Kuznia, K., Doshi, K., Pal, K. K., Patel, M., Moradshahi, M., Parmar, M., Purohit, M., Varshney, N., Kaza, P. R., Verma, P., Puri, R. S., Karia, R., Doshi, S., Sampat, S. K., Mishra, S., Reddy A, S., Patro, S., Dixit, T., & Shen, X.
- Affiliations
- University of Washington (US)
- University of Massachusetts Amherst (US)
- Sharif University of Technology (IR)
- Amirkabir University of Technology (IR)
- Indian Institute of Technology Kharagpur (IN)
- PSG College of Technology (IN)
- Arizona State University (US)
- University of Amsterdam (NL)
- University of California, Berkeley (US)
- Columbia University (US)
- Government Polytechnic College, Rajkot (IN)
- Microsoft (United States) (US)
- Stanford University (US)
- Tata Consultancy Services (India) (IN)
- Indian Institute of Technology Madras (IN)
- National University of Singapore (SG)
- ORCID
- Venue
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Published
- 2022
- Pages
- 5085-5109
- OpenAlex
- W4385572845
- Cited by
- 247
- References
- 51
- Citations by year
- 2026: 37 · 2025: 58 · 2024: 64 · 2023: 88
- FWCI
- 29.01
- 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)
- Funders
- Arizona State University
- 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)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- instruction tuning, large language models, natural language instructions
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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