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 this https URL .

在大型语言模型时代，我们提出了创新的协作学习框架 FreeAL，通过与 LLMs 交互式提取和过滤任务特定知识，显著提高了 SLM 和 LLM 的零样本性能，无需人工监督。