In this paper, we aim to generate text classification data given arbitrary
class definitions (i.e., user instruction), so one can train a small text
classifier without any human annotation or raw corpus. Compared with pioneer
attempts, our proposed Incubator is the first framework that can handle
complicated and even mutually dependent classes (e.g., "TED Talk given by
Educator" and "Other"). Specifically, Incubator is an LLM firstly tuned on the
instruction-to-data mappings that we obtained from classification datasets and
descriptions on HuggingFace together with in-context augmentation by GPT-4. We
then refine Incubator by learning on the cluster centers of semantic textual
embeddings to emphasize the uniformity and semantic diversity in generations.
We compare Incubator on various classification tasks with strong baselines such
as direct LLM-based inference and training data generation by prompt
engineering. Experiments show Incubator is able to (1) perform well on
traditional benchmarks, (2) take label dependency and user preference into
consideration, and (3) enable logical text mining by incubating multiple
classifiers.

我们的研究旨在通过用户指令生成文本分类数据，从而可以训练一个小型文本分类器，而无需任何人工注释或原始语料库。我们的提出的孵化器是第一个可以处理复杂甚至是相互依赖类别 (如 “教育家发表的 TED 演讲” 和 “其他”) 的框架，通过在分类数据集上获得的指导数据映射和 HuggingFace 上的描述，以及通过 GPT-4 进行上下文增强来首先对 LLM 进行调整。然后，我们在语义文本嵌入的簇中心上训练孵化器，以强调生成中的一致性和语义多样性。实验表明，与直接基于 LLM 的推理和通过提示工程生成训练数据的强基线相比，孵化器在各种分类任务上表现出色，并且能够考虑标签依赖性和用户偏好，并通过孵化多个分类器实现逻辑文本挖掘。