Cross-Task Generalization via Natural Language Crowdsourcing Instructions

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. NLP models built with the conventional paradigm, however, 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. The instructions are obtained from crowdsourcing instructions used to create existing NLP datasets and mapped to a unified schema. 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. These models, however, are far behind supervised task-specific models, indicating significant room for more progress in this direction.