The task of annotating data into concise summaries poses a significant challenge across various domains, frequently requiring the allocation of significant time and specialized knowledge by human experts. Despite existing efforts to use large language models for annotation tasks, significant problems such as limited applicability to unlabeled data, the absence of self-supervised methods, and the lack of focus on complex structured data still persist. In this work, we propose a GPT self-supervision annotation method, which embodies a generating-recovering paradigm that leverages the one-shot learning capabilities of the Generative Pretrained Transformer (GPT). The proposed approach comprises a one-shot tuning phase followed by a generation phase. In the one-shot tuning phase, we sample a data from the support set as part of the prompt for GPT to generate a textual summary, which is then used to recover the original data. The alignment score between the recovered and original data serves as a self-supervision navigator to refine the process. In the generation stage, the optimally selected one-shot sample serves as a template in the prompt and is applied to generating summaries from challenging datasets. The annotation performance is evaluated by tuning several human feedback reward networks and by calculating alignment scores between original and recovered data at both sentence and structure levels. Our self-supervised annotation method consistently achieves competitive scores, convincingly demonstrating its robust strength in various data-to-summary annotation tasks.

翻译：将数据标注为简洁摘要的任务在多个领域均构成重大挑战，通常需要人类专家投入大量时间和专业知识。尽管已有研究尝试使用大型语言模型进行标注，但依然存在显著问题，例如对未标注数据的适用性有限、缺乏自监督方法，以及对复杂结构化数据关注不足等。本文提出一种GPT自监督标注方法，该方法体现了“生成-恢复”范式，并利用了生成式预训练Transformer（GPT）的一次学习能力。该方案包含一个一次性调优阶段和一个生成阶段。在一次性调优阶段，我们从支持集中采样一个数据作为部分提示词输入GPT以生成文本摘要，随后利用该摘要恢复原始数据。恢复数据与原始数据之间的对齐分数作为自监督导航器来优化流程。在生成阶段，优化选择的一次性样本作为提示词模板，应用于从高难度数据集生成摘要。标注性能通过调整多个人类反馈奖励网络，并在句子和结构层面计算原始数据与恢复数据之间的对齐分数进行评测。我们的自监督标注方法持续获得具有竞争力的分数，充分证明了其在各类数据到摘要标注任务中的稳健性。