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We introduce STREET, a unified multi-task and multi-domain natural languagereasoning and explanation benchmark. Unlike most existing question-answering(QA) datasets, we expect models to not only answer questions, but also producestep-by-step structured explanations describing how premises in the questionare used to produce intermediate conclusions that can prove the correctness ofa certain answer. We perform extensive evaluation with popular language modelssuch as few-shot prompting GPT-3 and fine-tuned T5. We find that these modelsstill lag behind human performance when producing such structured reasoningsteps. We believe this work will provide a way for the community to bettertrain and test systems on multi-step reasoning and explanations in naturallanguage.

STREET: A Multi-Task Structured Reasoning and Explanation Benchmark