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. This method embodies a generating-recovering paradigm that leverages the capabilities of one-shot learning capabilities in 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构成重大挑战，通常需要人类专家投入大量时间与专业知识。尽管已有研究尝试利用大语言模型执行标注任务，但仍存在诸多显著问题：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的关注不足。本文提出一种GPT自监督标注方法，该方法采用生成-恢复范式，充分发挥生成式预训练Transformer（GPT）在单样本学习中的能力。所提方案包含单样本调优阶段与生成阶段。在单样本调优阶段，我们从支持集中采样数据作为提示词的一部分，引导GPT生成文本摘要，再利用该摘要恢复原始数据。通过计算恢复数据与原始数据之间的对齐分数，该分数作为自监督导航指标以优化流程。在生成阶段，最优单样本被选为提示词模板，用于从复杂数据集中生成摘要。标注性能通过调优多个人类反馈奖励网络，并在句子与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估。我们的自监督标注方法在各类数据至摘要标注任务中持续取得具有竞争力的分数，有力证明了其鲁棒性。