![]() This data is used in the human evaluation section of the Self Instruct paper. ![]() The authors also released a new set of 252 expert-written tasks and their instructions motivated by user-oriented applications (rather than well-studied NLP tasks). This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. With Self-Instruct, it is possible to improve the instruction-following capabilities of language models without relying on extensive manual annotation.Ī part of this framework, the Self-Instruct authors released a dataset that contains 52k instructions, paired with 82K instance inputs and outputs. It does this by using the model's own generations to create a large collection of instructional data. Self-Instruct is a framework that helps language models improve their ability to follow natural language instructions.
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