Abstract
The goal of open relation extraction (OpenRE) is to develop an RE model that can generalize to new relations not encountered during training. Existing studies primarily formulate OpenRE as a clustering task. They first cluster all test instances based on the similarity between the instances, and then manually assign a new relation to each cluster. However, their reliance on human annotation limits their practicality. In this paper, we propose an OpenRE framework based on large language models (LLMs), which directly predicts new relations for test instances by leveraging their strong language understanding and generation abilities, without human intervention. Specifically, our framework consists of two core components: (1) a relation discoverer (RD), designed to predict new relations for test instances based on \textit{demonstrations} formed by training instances with known relations; and (2) a relation predictor (RP), used to select the most likely relation for a test instance from $n$ candidate relations, guided by \textit{demonstrations} composed of their instances. To enhance the ability of our framework to predict new relations, we design a self-correcting inference strategy composed of three stages: relation discovery, relation denoising, and relation prediction. In the first stage, we use RD to preliminarily predict new relations for all test instances. Next, we apply RP to select some high-reliability test instances for each new relation from the prediction results of RD through a cross-validation method. During the third stage, we employ RP to re-predict the relations of all test instances based on the demonstrations constructed from these reliable test instances. Extensive experiments on three OpenRE datasets demonstrate the effectiveness of our framework. We release our code at this https URL.
Abstract (translated)
开放关系抽取（Open Relation Extraction，简称OpenRE）的目标是开发出能够泛化到训练过程中未曾遇到的新关系的实体关系模型。现有研究主要将OpenRE视为一个聚类任务，即先根据实例间的相似度对所有测试用例进行聚类，然后人工为每个簇分配一个新的关系。然而，这种依赖于人力标注的方法限制了其实际应用性。 本文提出了一种基于大规模语言模型（Large Language Models, LLMs）的OpenRE框架，该框架能够直接利用LLMs强大的语言理解和生成能力预测测试实例的新关系，无需人工干预。具体而言，我们的框架包含两个核心组件： 1. 关系发现器 (Relation Discoverer, RD)：用于基于训练实例与已知关系形成的“示例”来预测新关系。 2. 关系预测器 (Relation Predictor, RP)：用于从 $n$ 个候选关系中选择最有可能的关系，通过构成这些实例的“示例”进行指导。 为了增强我们框架预测新关系的能力，设计了一种三阶段自校正推理策略，包括：关系发现、去噪和关系预测。在第一阶段，使用RD初步为所有测试用例预测新关系。接下来，在第二阶段中，利用RP通过交叉验证方法从RD的预测结果中挑选出每个新关系的一些高可信度的测试实例。最后，在第三阶段，使用RP基于这些可靠测试用例构建的“示例”重新预测所有测试用例的关系。 在三个OpenRE数据集上的广泛实验展示了我们框架的有效性，并且我们在[这个链接](https://this.http URL/)发布了我们的代码。
URL
https://arxiv.org/abs/2509.15089
