Instruction tuning enables language models to more effectively generalize and better follow user intent. However, obtaining instruction data is costly and challenging. Prior work employs methods such as expensive human annotation, crowd-sourced datasets with alignment issues, and generating noisy examples via LLMs. We introduce the LongForm-C dataset, which is created by reverse instructions. We generate instructions via LLMs for human-written corpus examples using reverse instructions. First we select a diverse set of human-written documents from corpora such as C4 and Wikipedia; then we generate instructions for these documents via LLMs. This approach provides a cheaper and cleaner instruction-tuning dataset with natural output and one suitable for long text generation. Our models outperform 10x larger language models without instruction tuning on tasks such as story/recipe generation and long-form question answering. Moreover, LongForm models outperform prior instruction-tuned models such as FLAN-T5 and Alpaca by a large margin, and improve language understanding capabilities further. Finally, our models can effectively follow and answer multilingual instructions; we demonstrate this for news generation. We publicly release our data and models: https://github.com/akoksal/LongForm.

翻译：指令微调可使语言模型更有效地泛化并更好地遵循用户意图。然而，获取指令数据的成本高昂且充满挑战。以往的工作采用了昂贵的人工标注、存在对齐问题的众包数据集，以及通过大语言模型生成含噪声样本等方法。我们引入了通过反向指令创建的LongForm-C数据集。我们利用大语言模型为人工撰写的语料库样本生成基于反向指令的指令。首先，我们从C4和Wikipedia等语料库中选取多样化的人工撰写文档；然后通过大语言模型为这些文档生成指令。该方法能以更低成本提供更清洁的指令微调数据集，其输出自然且适用于长文本生成。在故事/食谱生成和长文本问答等任务中，我们的模型性能优于未经过指令微调的10倍规模语言模型。此外，LongForm模型在性能上大幅超越FLAN-T5和Alpaca等先前指令微调模型，并进一步提升了语言理解能力。最后，我们的模型能够有效遵循并回答多语言指令——我们以新闻生成为例进行了验证。我们已公开发布数据和模型：https://github.com/akoksal/LongForm。