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Instruction tuning enables language models to generalize more effectively andbetter follow user intent. However, obtaining instruction data can be costlyand challenging. Prior works employ methods such as expensive human annotation,crowd-sourced datasets with alignment issues, or generating noisy examples viaLLMs. We introduce the LongForm dataset, which is created by leveraging Englishcorpus examples with augmented instructions. We select a diverse set ofhuman-written documents from existing corpora such as C4 and Wikipedia andgenerate instructions for the given documents via LLMs. This approach providesa cheaper and cleaner instruction-tuning dataset and one suitable for long textgeneration. We finetune T5, OPT, and LLaMA models on our dataset and show thateven smaller LongForm models have good generalization capabilities for textgeneration. Our models outperform 10x larger language models withoutinstruction tuning on various tasks such as story/recipe generation andlong-form question answering. Moreover, LongForm models outperform priorinstruction-tuned models such as FLAN-T5 and Alpaca by a large margin. Finally,our models can effectively follow and answer multilingual instructions; wedemonstrate this for news generation. We publicly release our data and models:https://github.com/akoksal/LongForm.

LongForm: Optimizing Instruction Tuning for Long Text Generation with Corpus Extraction