Although large language models (LLMs) have advanced the state-of-the-art in
NLP significantly, deploying them for downstream applications is still
challenging due to cost, responsiveness, control, or concerns around privacy
and security. As such, trainable models are still the preferred option in some
cases. However, these models still require human-labeled data for optimal
performance, which is expensive and time-consuming to obtain. In order to
address this issue, several techniques to reduce human effort involve labeling
or generating data using LLMs. Although these methods are effective for certain
applications, in practice they encounter difficulties in real-world scenarios.
Labeling data requires careful data selection, while generating data
necessitates task-specific prompt engineering. In this paper, we propose a
unified data creation pipeline that requires only a single formatting example,
and which is applicable to a broad range of tasks, including traditionally
problematic ones with semantically devoid label spaces. In our experiments we
demonstrate that instruction-following LLMs are highly cost-effective data
creators, and that models trained with these data exhibit performance better
than those trained with human-labeled data (by up to 17.5%) on
out-of-distribution evaluation, while maintaining comparable performance on
in-distribution tasks. These results have important implications for the
robustness of NLP systems deployed in the real-world.

本文提出了一种统一的数据创建流程，只需一个格式示例，适用于包括传统上问题较多的任务在内的广泛范围，通过实验证明使用指令跟随型大型语言模型创建的数据比使用人工标注的数据在分布外评估上表现更好（高达 17.5%），同时在分布内任务上保持可比较的性能，这些结果对于在现实世界中部署的自然语言处理系统的稳健性具有重要意义。