Large Language Models (LLMs) have demonstrated exceptional abilities in comprehending and generating text, motivating numerous researchers to utilize them for Information Extraction (IE) purposes, including Relation Extraction (RE). Nonetheless, most existing methods are predominantly designed for Sentence-level Relation Extraction (SentRE) tasks, which typically encompass a restricted set of relations and triplet facts within a single sentence. Furthermore, certain approaches resort to treating relations as candidate choices integrated into prompt templates, leading to inefficient processing and suboptimal performance when tackling Document-Level Relation Extraction (DocRE) tasks, which entail handling multiple relations and triplet facts distributed across a given document, posing distinct challenges. To overcome these limitations, we introduce AutoRE, an end-to-end DocRE model that adopts a novel RE extraction paradigm named RHF (Relation-Head-Facts). Unlike existing approaches, AutoRE does not rely on the assumption of known relation options, making it more reflective of real-world scenarios. Additionally, we have developed an easily extensible RE framework using a Parameters Efficient Fine Tuning (PEFT) algorithm (QLoRA). Our experiments on the RE-DocRED dataset showcase AutoRE's best performance, achieving state-of-the-art results, surpassing TAG by 10.03\% and 9.03\% respectively on the dev and test set. The code is available at https://github.com/THUDM/AutoRE and the demonstration video is provided at https://www.youtube.com/watch?v=IhKRsZUAxKk.

翻译：大语言模型（LLMs）在文本理解与生成方面展现出卓越能力，促使众多研究者将其应用于信息抽取（IE）任务，包括关系抽取（RE）。然而，现有方法主要针对句子级关系抽取（SentRE）任务设计，这类任务通常仅涉及单个句子中的有限关系集合与三元组事实。此外，部分方法将关系视为嵌入提示模板的候选选项，在处理文档级关系抽取（DocRE）任务时效率低下且性能欠佳——DocRE需要处理分布于整篇文档的多种关系与三元组事实，构成独特挑战。为突破这些局限，我们提出AutoRE，一种采用新型关系抽取范式RHF（关系-头实体-事实）的端到端DocRE模型。与现有方法不同，AutoRE不依赖已知关系选项的假设，从而更贴合实际应用场景。同时，我们基于参数高效微调（PEFT）算法QLoRA构建了易于扩展的关系抽取框架。在RE-DocRED数据集上的实验表明，AutoRE取得最佳性能，在开发集和测试集上分别以10.03%和9.03%的优势超越TAG模型，达到当前最优水平。代码发布于https://github.com/THUDM/AutoRE，演示视频详见https://www.youtube.com/watch?v=IhKRsZUAxKk。