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：[translated abstract in Chinese] 大语言模型(LLMs)如GPT-3和ChatGPT，通过情境学习(in-context learning)在各类自然语言处理任务中展现了卓越性能，该方法基于少量示范示例进行推理。尽管在自然语言处理任务中取得了成功，但目前尚无研究评估LLMs通过情境学习执行文档信息抽取(DIE)的能力。将LLMs应用于文档信息抽取面临两大挑战：模态差异和任务差异。为此，我们提出一个简单而有效的情境学习框架ICL-D3IE，该框架使LLMs能够利用不同类型的示范示例执行文档信息抽取。具体而言，我们从困难训练文档中提取最具挑战性和区分度的片段作为硬示范，以利于所有测试实例；设计描述位置关系的示范示例，使LLMs能够理解位置关系；引入格式化示范以简化答案抽取。此外，该框架通过迭代更新多样化示范来持续改进。在三个广泛使用的基准数据集上的实验表明，相比于采用完整训练数据微调的传统预训练方法，ICL-D3IE框架使GPT-3/ChatGPT在分布内(ID)和分布外(OOD)设置下均取得了更优性能。