Collecting high-quality labeled data for model training is notoriously time-consuming and labor-intensive for various NLP tasks. While copious solutions, such as active learning for small language models (SLMs) and prevalent in-context learning in the era of large language models (LLMs), have been proposed and alleviate the labeling burden to some extent, their performances are still subject to human intervention. It is still underexplored how to reduce the annotation cost in the LLMs era. To bridge this, we revolutionize traditional active learning and propose an innovative collaborative learning framework FreeAL to interactively distill and filter the task-specific knowledge from LLMs. During collaborative training, an LLM serves as an active annotator inculcating its coarse-grained knowledge, while a downstream SLM is incurred as a student to filter out high-quality in-context samples to feedback LLM for the subsequent label refinery. Extensive experiments on eight benchmark datasets demonstrate that FreeAL largely enhances the zero-shot performances for both SLM and LLM without any human supervision. The code is available at https://github.com/Justherozen/FreeAL .

翻译：为各种自然语言处理任务收集高质量标注数据用于模型训练，通常耗时且劳动密集。尽管已有诸多解决方案（如面向小语言模型的主动学习，以及大语言模型时代广泛采用的上下文学习）在一定程度上缓解了标注负担，但它们的性能仍受制于人工干预。如何在大语言模型时代降低标注成本仍是尚未充分探索的问题。为此，我们彻底革新传统主动学习，提出创新协作学习框架FreeAL，通过交互式蒸馏与过滤从大语言模型中提取任务特定知识。在协作训练过程中，大语言模型作为主动标注者输出粗粒度知识，而下游小语言模型则作为学生筛选高质量上下文样本，反馈给大语言模型用于后续标签精炼。在八个基准数据集上的大量实验表明，FreeAL无需任何人工监督即可显著提升小语言模型与大语言模型的零样本性能。代码见https://github.com/Justherozen/FreeAL。