Update README.md

README.md

@@ -37,4 +37,57 @@ These tasks are:
 5. 'task033_winogrande_answer_generation', # Answering a fill in the blank question on objects.	
 6. 'task034_winogrande_question_modification_object', # Modifying a fill in the blank question on objects.	
 7. 'task035_winogrande_question_modification_person', # Modifying a fill in the blank question on persons.	
-8. 'task1391_winogrande_easy_answer_generation', # Answering a fill in the blank question on objects.
+8. 'task1391_winogrande_easy_answer_generation', # Answering a fill in the blank question on objects.
+
+```
+@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.",
+}
+```