Large language models (LLMs), such as GPT-3 and ChatGPT, have demonstrated remarkable results in various natural language processing (NLP) tasks with in-context learning, which involves inference based on a few demonstration examples. Despite their successes in NLP tasks, no investigation has been conducted to assess the ability of LLMs to perform document information extraction (DIE) using in-context learning. Applying LLMs to DIE poses two challenges: the modality and task gap. To this end, we propose a simple but effective in-context learning framework called ICL-D3IE, which enables LLMs to perform DIE with different types of demonstration examples. Specifically, we extract the most difficult and distinct segments from hard training documents as hard demonstrations for benefiting all test instances. We design demonstrations describing relationships that enable LLMs to understand positional relationships. We introduce formatting demonstrations for easy answer extraction. Additionally, the framework improves diverse demonstrations by updating them iteratively. Our experiments on three widely used benchmark datasets demonstrate that the ICL-D3IE framework enables GPT-3/ChatGPT to achieve superior performance when compared to previous pre-trained methods fine-tuned with full training in both the in-distribution (ID) setting and in the out-of-distribution (OOD) setting.

翻译：大型语言模型（如GPT-3和ChatGPT）通过上下文学习在多项自然语言处理任务中展现了显著成果，该方法基于少量示例进行推理。尽管在自然语言处理任务中取得成功，但尚未有研究评估大型语言模型通过上下文学习执行文档信息提取的能力。将大语言模型应用于文档信息提取面临两大挑战：模态差距和任务差距。为此，我们提出一个简洁而有效的上下文学习框架ICL-D3IE，使大语言模型能够利用不同类型的示例进行文档信息提取。具体而言，我们从困难训练文档中提取最具难度和区分度的片段作为困难示例，以惠及所有测试实例；设计描述位置关系的示例使大语言模型理解空间关系；引入格式化示例便于答案抽取。此外，该框架通过迭代更新示例来增强其多样性。在三个广泛使用的基准数据集上的实验表明，在分布内和分布外场景下，ICL-D3IE框架使GPT-3/ChatGPT在性能上超越先前需全量微调的预训练方法。