@inproceedings{wang-etal-2022-super,
title = "Super-{N}atural{I}nstructions: Generalization via Declarative Instructions on 1600+ {NLP} Tasks",
author = "Wang, Yizhong and
Mishra, Swaroop and
Alipoormolabashi, Pegah and
Kordi, Yeganeh and
Mirzaei, Amirreza and
Naik, Atharva and
Ashok, Arjun and
Dhanasekaran, Arut Selvan and
Arunkumar, Anjana and
Stap, David and
Pathak, Eshaan and
Karamanolakis, Giannis and
Lai, Haizhi and
Purohit, Ishan and
Mondal, Ishani and
Anderson, Jacob and
Kuznia, Kirby and
Doshi, Krima and
Pal, Kuntal Kumar and
Patel, Maitreya and
Moradshahi, Mehrad and
Parmar, Mihir and
Purohit, Mirali and
Varshney, Neeraj and
Kaza, Phani Rohitha and
Verma, Pulkit and
Puri, Ravsehaj Singh and
Karia, Rushang and
Doshi, Savan and
Sampat, Shailaja Keyur and
Mishra, Siddhartha and
Reddy A, Sujan and
Patro, Sumanta and
Dixit, Tanay and
Shen, Xudong",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.340",
doi = "10.18653/v1/2022.emnlp-main.340",
pages = "5085--5109",
abstract = "How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions{---}training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones. Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9{\%} on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models.",
}
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<abstract>How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions—training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones. Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9% on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models.</abstract>
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%0 Conference Proceedings
%T Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
%A Wang, Yizhong
%A Mishra, Swaroop
%A Alipoormolabashi, Pegah
%A Kordi, Yeganeh
%A Mirzaei, Amirreza
%A Naik, Atharva
%A Ashok, Arjun
%A Dhanasekaran, Arut Selvan
%A Arunkumar, Anjana
%A Stap, David
%A Pathak, Eshaan
%A Karamanolakis, Giannis
%A Lai, Haizhi
%A Purohit, Ishan
%A Mondal, Ishani
%A Anderson, Jacob
%A Kuznia, Kirby
%A Doshi, Krima
%A Pal, Kuntal Kumar
%A Patel, Maitreya
%A Moradshahi, Mehrad
%A Parmar, Mihir
%A Purohit, Mirali
%A Varshney, Neeraj
%A Kaza, Phani Rohitha
%A Verma, Pulkit
%A Puri, Ravsehaj Singh
%A Karia, Rushang
%A Doshi, Savan
%A Sampat, Shailaja Keyur
%A Mishra, Siddhartha
%A Reddy A, Sujan
%A Patro, Sumanta
%A Dixit, Tanay
%A Shen, Xudong
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F wang-etal-2022-super
%X How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions—training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones. Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9% on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models.
%R 10.18653/v1/2022.emnlp-main.340
%U https://aclanthology.org/2022.emnlp-main.340
%U https://doi.org/10.18653/v1/2022.emnlp-main.340
%P 5085-5109
Markdown (Informal)
[Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://aclanthology.org/2022.emnlp-main.340) (Wang et al., EMNLP 2022)
ACL
- 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, et al.. 2022. Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 5085–5109, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.