Bonito introduces a model for conditional task generation to convert unannotated text into instruction tuning datasets. The goal is to enable zero-shot task adaptation of large language models on specialized, private data. Bonito significantly improves the performance of pretrained and instruction tuned models over self-supervised baselines. By generating synthetic tasks for various datasets across different task types, Bonito shows promising results in adapting language models to new domains effectively. The study focuses on the importance of learning with synthetic instruction tuning datasets as an alternative to self-supervision.
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