Large language models (LLMs) pre-trained on massive corpora have demonstrated impressive few-shot learning capability on many NLP tasks. Recasting an NLP task into a text-to-text generation task is a common practice so that generative LLMs can be prompted to resolve it. However, performing document-level relation extraction (DocRE) tasks with generative LLM models is still challenging due to the structured output format of DocRE, which complicates the conversion to plain text. Limited information available in few-shot samples and prompt instructions induce further difficulties and challenges in relation extraction for mentioned entities in a document. In this paper, we represent the structured output as a graph-style triplet rather than natural language expressions and leverage generative LLMs for the DocRE task. Our approach, the Graph-DPEP framework is grounded in the reasoning behind triplet explanation thoughts presented in natural language. In this framework, we first introduce a ``decomposed-plug" method for performing the generation from LLMs over prompts with type-space decomposition to alleviate the burden of distinguishing all relation types. Second, we employ a verifier for calibrating the generation and identifying overlooked query entity pairs. Third, we develop "ensemble-play", reapplying generation on the entire type list by leveraging the reasoning thoughts embedded in a sub-graph associated with the missing query pair to address the missingness issue. Through extensive comparisons with existing prompt techniques and alternative Language Models (LLMs), our framework demonstrates superior performance on publicly available benchmarks in experiments.

翻译：在大规模语料上预训练的大型语言模型（LLM）已在众多自然语言处理任务中展现出卓越的小样本学习能力。将自然语言处理任务重构为文本到文本的生成任务是一种常见做法，从而可以通过提示生成式LLM来解决问题。然而，由于文档级关系抽取（DocRE）任务的结构化输出格式难以转换为纯文本，使用生成式LLM完成该任务仍具挑战性。小样本中有限的信息以及提示指令进一步增加了文档中提及实体的关系抽取的难度与挑战。本文提出将结构化输出表示为图式三元组而非自然语言表达，并利用生成式LLM完成DocRE任务。我们的方法——Graph-DPEP框架——基于以自然语言呈现的三元组解释思维的推理过程。该框架首先引入一种“分解即插”方法，通过在类型空间分解的提示下执行LLM生成，以减轻区分所有关系类型的负担。其次，我们采用验证器对生成结果进行校准并识别被忽略的查询实体对。第三，我们开发了“集成推理”机制，通过利用与缺失查询对相关的子图中嵌入的推理思维，在整个类型列表上重新执行生成，以解决缺失问题。通过与现有提示技术及其他语言模型（LLM）的广泛对比实验，我们的框架在公开基准测试中展现了优越的性能。