Cross-Task Generalization via Natural Language Crowdsourcing Instructions

Swaroop Mishra, Daniel Khashabi, Chitta Baral, Hannaneh Hajishirzi


Abstract
Humans (e.g., crowdworkers) have a remarkable ability in solving different tasks, by simply reading textual instructions that define them and looking at a few examples. Despite the success of the conventional supervised learning on individual datasets, such models often struggle with generalization across tasks (e.g., a question-answering system cannot solve classification tasks). A long-standing challenge in AI is to build a model that learns a new task by understanding the human-readable instructions that define it. To study this, we introduce NATURAL INSTRUCTIONS, a dataset of 61 distinct tasks, their human-authored instructions, and 193k task instances (input-output pairs). The instructions are obtained from crowdsourcing instructions used to create existing NLP datasets and mapped to a unified schema. Using this meta-dataset, we measure cross-task generalization by training models on seen tasks and measuring generalization to the remaining unseen ones. We adopt generative pre-trained language models to encode task-specific instructions along with input and generate task output. Our results indicate that models benefit from instructions when evaluated in terms of generalization to unseen tasks (19% better for models utilizing instructions). These models, however, are far behind an estimated performance upperbound indicating significant room for more progress in this direction.
Anthology ID:
2022.acl-long.244
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Smaranda Muresan, Preslav Nakov, Aline Villavicencio
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3470–3487
Language:
URL:
https://aclanthology.org/2022.acl-long.244
DOI:
10.18653/v1/2022.acl-long.244
Bibkey:
Cite (ACL):
Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hannaneh Hajishirzi. 2022. Cross-Task Generalization via Natural Language Crowdsourcing Instructions. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3470–3487, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Cross-Task Generalization via Natural Language Crowdsourcing Instructions (Mishra et al., ACL 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/naacl24-info/2022.acl-long.244.pdf
Video:
 https://preview.aclanthology.org/naacl24-info/2022.acl-long.244.mp4
Code
 allenai/natural-instructions +  additional community code
Data
Natural InstructionsCosmosQADROPMC-TACOQASCQuorefWinoGrande