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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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Self-citation
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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