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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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“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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Hua, A., Tang, K., Gu, C., Gu, J., Wong, E., & Qin, Y. (2025) · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2025.emnlp-main.1006 · Cited by 28
RA-DIT: Retrieval-Augmented Dual Instruction Tuning
Lin, X. V., Chen, X., Chen, M., Shi, W., Lomeli, M., James, R., Rodriguez, P., Kahn, J., Szilvasy, G., Lewis, M., et al. (2023) · arXiv · doi:10.48550/arxiv.2310.01352 · Cited by 26
MOSS: An Open Conversational Large Language Model
Sun, T., Zhang, X., He, Z., Li, P., Cheng, Q., Liu, X., Yan, H., Shao, Y., Tang, Q., Zhang, S., et al. (2024) · Machine Intelligence Research · doi:10.1007/s11633-024-1502-8 · Cited by 48
CoEdIT: Text Editing by Task-Specific Instruction Tuning
Raheja, V., Kumar, D., Koo, R., & Kang, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.350 · Cited by 62
Qiu, X., Hao, T., Shi, S., Tan, X., & Xiong, Y.-J. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3377590 · Cited by 28
CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models
Qian, C., Han, C., Fung, Y., Qin, Y., Liu, Z., & Ji, H. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.462 · Cited by 24
Feng, K., Luo, L., Xia, Y., Luo, B., He, X., Li, K., Zha, Z., Xu, B., & Peng, K. (2024) · Symmetry · doi:10.3390/sym16111470 · Cited by 25
Transfer learning for language model adaptation
Saiful, B. M. (2023) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/169892 · Cited by 22
SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Prompting
Zhang, X., Peng, B., Li, K., Zhou, J., & Meng, H. M. L. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.891 · Cited by 43
Instruct and Extract: Instruction Tuning for On-Demand Information Extraction
Jiao, Y., Zhong, M., Li, S., Zhao, R., Ouyang, S., Ji, H., & Han, J. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.620 · Cited by 33
A Survey of LLM Datasets: From Autoregressive Model to AI Chatbot
Du, F., Ma, X.-J., Yang, J., Liu, Y., Luo, C.-R., Wang, X., Jiang, H.-O., & Jing, X. (2024) · Journal of Computer Science and Technology · doi:10.1007/s11390-024-3767-3 · Cited by 25
Tc-llama 2: fine-tuning LLM for technology and commercialization applications
Yeom, J., Lee, H., Byun, H., Kim, Y., Byun, J., Choi, Y., Kim, S., & Song, K. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-00963-0 · Cited by 21
So you want your private LLM at home? A survey and benchmark of methods for efficient GPTs
Tuggener, L., Sager, P., Taoudi-Benchekroun, Y., Grewe, B. F., & Stadelmann, T. (2024) · IEEE Swiss Conference on Data Science (SDS) · doi:10.1109/sds60720.2024.00036 · Cited by 23
On the potential of large language models to solve semantics-aware process mining tasks
Rebmann, A., Schmidt, F. D., Glavaš, G., & van der Aa, H. (2025) · Process Science · doi:10.1007/s44311-025-00019-3 · Cited by 25
A Hybrid Multi-Agent Conversational Recommender System with LLM and Search Engine in E-commerce
Nie, G., Zhi, R., Yan, X., Du, Y., Zhang, X., Chen, J., Zhou, M., Chen, H., Li, T., Cheng, Z., et al. (2024) · 18th ACM Conference on Recommender Systems · doi:10.1145/3640457.3688061 · Cited by 15
Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation
Da, Y., Liu, X., Yin, F., Zhong, M., Bansal, H., Han, J., & Chang, K.-W. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.245 · Cited by 41
Demystifying Data Management for Large Language Models
Miao, X., Jia, Z., & Cui, B. (2024) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3626246.3654683 · Cited by 22
OctoPack: Instruction Tuning Code Large Language Models
Muennighoff, N., Liu, Q., Zebaze, A., Zheng, Q., Hui, B., Zhuo, T. Y., Singh, S., Tang, X., von Werra, L., & Longpre, S. (2023) · arXiv · doi:10.48550/arxiv.2308.07124 · Cited by 245
“Training models to follow instructions has led to new capabilities in text (Ouyang et al., 2022; Wang et al., 2022b; Chung et al., 2022) and visual modalities (Xu et al., 2023b; OpenAI, 2023).”
BackgroundMethodInfluentialUnderstanding the Effects of RLHF on LLM Generalisation and Diversity
Kirk, R., Mediratta, I., Nalmpantis, C., Luketina, J., Hambro, E., Grefenstette, E., & Raileanu, R. (2023) · arXiv · doi:10.48550/arxiv.2310.06452 · Cited by 17
Towards Incremental Learning in Large Language Models: A Critical Review
Jovanovic, M., & Voss, P. (2025) · Expert Systems · doi:10.1111/exsy.70127 · Cited by 32
Agent Lumos: Unified and Modular Training for Open-Source Language Agents
Yin, D., Brahman, F., Ravichander, A., Chandu, K., Chang, K.-W., Choi, Y., & Lin, B. Y. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.670 · Cited by 17
CoPrompter: User-Centric Evaluation of LLM Instruction Alignment for Improved Prompt Engineering
Joshi, I., Shahid, S., Venneti, S., Vasu, M., Zheng, Y., Li, Y., Krishnamurthy, B., & Chan, G. Y.-Y. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712102 · Cited by 24
Specialist or Generalist? Instruction Tuning for Specific NLP Tasks
Shi, C., Su, Y., Yang, C. H., Yang, Y., & Cai, D. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.947 · Cited by 24
trlX: A Framework for Large Scale Reinforcement Learning from Human Feedback
Havrilla, A., Zhuravinskyi, M., Phung, D., Tiwari, A. K., Tow, J., Biderman, S., Anthony, Q. G., & Castricato, L. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.530 · Cited by 30
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
Min, S., Krishna, K., Lyu, X., Lewis, M., Yih, W.-T., Koh, P. W., Iyyer, M., Zettlemoyer, L., & Hajishirzi, H. (2023) · arXiv · doi:10.48550/arxiv.2305.14251 · Cited by 14
Self-citationAffective computing in the era of large language models: A survey from the NLP perspective
Zhang, Y., Yang, X., Xu, X., Gao, Z., Huang, Y., Mu, S., Feng, S., Wang, D., Zhang, Y., Song, K., et al. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115411 · Cited by 28
Wu, C., Ke, W., Wang, P., Luo, Z., Li, G., & Chen, W. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29892 · Cited by 16
Exploring biases related to the use of large language models in a multilingual depression corpus
Pérez-Toro, P. A., Dineley, J., Iniesta, R., Zhang, Y., Matcham, F., Siddi, S., Lamers, F., Haro, J. M., Penninx, B. W. J. H., Folarin, A., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-19980-x · Cited by 18
Toward Human Readable Prompt Tuning: Kubrick’s The Shining is a good movie, and a good prompt too?
Shi, W., Han, X., Gonen, H., Holtzman, A., Tsvetkov, Y., & Zettlemoyer, L. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.733 · Cited by 44
KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection
Choi, S.-H., Fang, T., Wang, Z., & Song, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.867 · Cited by 46
C-STS: Conditional Semantic Textual Similarity
Deshpande, A., Jimenez, C., Chen, H., Murahari, V., Graf, V., Rajpurohit, T., Kalyan, A., Chen, D., & Narasimhan, K. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.345 · Cited by 17
LLM-Assisted Data Augmentation for Chinese Dialogue-Level Dependency Parsing
Zhang, M., Jiang, G., Liu, S., Chen, J., & Zhang, M. (2024) · Computational Linguistics · doi:10.1162/coli_a_00515 · Cited by 14
Multi-view fusion for instruction mining of large language model
Huang, H., Xu, B., Liang, X., Chen, K., Yang, M., Zhao, T., & Zhu, C. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102480 · Cited by 13
Discrete Prompt Compression With Reinforcement Learning
Jung, H., & Kim, K.-J. (2024) · IEEE Access · doi:10.1109/access.2024.3403426 · Cited by 29
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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