Most NLP tasks are modeled as supervised learning and thus require labeled training data to train effective models. However, manually producing such data at sufficient quality and quantity is known to be costly and time-intensive. Current research addresses this bottleneck by exploring a novel paradigm called zero-shot learning via dataset generation. Here, a powerful LLM is prompted with a task description to generate labeled data that can be used to train a downstream NLP model. For instance, an LLM might be prompted to "generate 500 movie reviews with positive overall sentiment, and another 500 with negative sentiment." The generated data could then be used to train a binary sentiment classifier, effectively leveraging an LLM as a teacher to a smaller student model. With this demo, we introduce Fabricator, an open-source Python toolkit for dataset generation. Fabricator implements common dataset generation workflows, supports a wide range of downstream NLP tasks (such as text classification, question answering, and entity recognition), and is integrated with well-known libraries to facilitate quick experimentation. With Fabricator, we aim to support researchers in conducting reproducible dataset generation experiments using LLMs and help practitioners apply this approach to train models for downstream tasks.

翻译：大多数自然语言处理任务被建模为监督学习，因此需要标注训练数据来训练有效模型。然而，以足够质量和数量手动生成此类数据已知既费时又昂贵。当前研究通过探索一种名为“基于数据集生成的零样本学习”的新范式来解决这一瓶颈。在此方法中，使用任务描述提示强大的大语言模型，生成可用于训练下游NLP模型的标注数据。例如，可提示大语言模型“生成500条整体情感正面的电影评论，以及500条情感负面的评论”。生成的数据随后可用于训练二元情感分类器，从而有效将大语言模型作为教师模型来训练较小的学生模型。通过本演示，我们介绍Fabricator——一个用于数据集生成的开源Python工具包。Fabricator实现了常见的数据集生成工作流，支持广泛的下游NLP任务（如文本分类、问答和实体识别），并与知名库集成以促进快速实验。借助Fabricator，我们旨在支持研究人员使用大语言模型开展可复现的数据集生成实验，并帮助从业者应用该方法训练下游任务模型。