学习生成用于零样本任务适配的指令微调数据集
Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation
AI总结：
本研究提出开源条件任务生成模型Bonito，通过元模板构建的大规模数据集训练，可将无标注文本转为指令微调数据集，助力大语言模型在专业私有数据上实现零样本任务适配，性能显著优于自监督基线。
摘要：
我们推出Bonito，一款用于条件任务生成的开源模型，可将无标注文本转换为用于指令微调的特定任务训练数据集。我们的目标是让大语言模型能够基于用户的专业私有数据实现零样本任务适配。我们通过将现有指令微调数据集重新混合为元模板，构建了一个包含165万个样本的新型大规模数据集，并在此数据集上对预训练大语言模型进行微调，从而训练出Bonito。一个数据集对应的元模板会生成训练样本，其中输入为无标注文本及任务属性，输出则由指令和响应组成。我们使用Bonito为来自专业领域的7个数据集生成合成任务，这些数据集包含无标注文本，覆盖三类任务——是非问答、抽取式问答和自然语言推理——并以此适配语言模型。我们的研究表明，相较于事实上的自监督基线，Bonito显著提升了预训练模型和指令微调模型的平均性能。例如，使用Bonito适配Mistral-Instruct-v2以及Mistral和Llama2的指令微调变体后，其原本优异的零样本性能提升了22.1个F1点；而下一词预测目标则会抵消指令微调的部分收益，使平均性能下降0.8个F1点。我们还利用Bonito开展了额外实验，以探究领域、训练集规模以及备选合成任务生成器的选择所带来的影响。总体而言，我们证明了使用合成指令微调数据集进行学习是让语言模型适配新领域的有效方法。模型、数据集和代码可在https://github.com/BatsResearch/bonito 获取。
英文摘要：
We introduce Bonito, an open-source model for conditional task generation that converts unannotated text into task-specific training datasets for instruction tuning. We aim to enable zero-shot task adaptation of large language models on users' specialized, private data. We train Bonito by fine-tuning a pretrained large language model on a new large-scale dataset with 1.65M examples created by remixing existing instruction tuning datasets into meta-templates. The meta-templates for a dataset produce training examples where the input is the unannotated text and the task attribute and the output consists of the instruction and the response. We use Bonito to generate synthetic tasks for seven datasets from specialized domains with unannotated text across three task types -- yes-no question answering, extractive question answering, and natural language inference -- and adapt language models. We show that Bonito significantly improves the average performance of pretrained and instruction tuned models over the de facto self supervised baseline. For example, adapting Mistral-Instruct-v2 and instruction tuned variants of Mistral and Llama2 with Bonito improves the strong zero-shot performance by 22.1 F1 points whereas the next word prediction objective undoes some of the benefits of instruction tuning and reduces the average performance by 0.8 F1 points. We conduct additional experiments with Bonito to understand the effects of the domain, the size of the training set, and the choice of alternative synthetic task generators. Overall, we show that learning with synthetic instruction tuning datasets is an effective way to adapt language models to new domains. The model, dataset, and code are available at https://github.com/BatsResearch/bonito.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
