In spite of the potential for ground-breaking achievements offered by large language models (LLMs) (e.g., GPT-3), they still lag significantly behind fully-supervised baselines (e.g., fine-tuned BERT) in relation extraction (RE). This is due to the two major shortcomings of LLMs in RE: (1) low relevance regarding entity and relation in retrieved demonstrations for in-context learning; and (2) the strong inclination to wrongly classify NULL examples into other pre-defined labels. In this paper, we propose GPT-RE to bridge the gap between LLMs and fully-supervised baselines. GPT-RE successfully addresses the aforementioned issues by (1) incorporating task-specific entity representations in demonstration retrieval; and (2) enriching the demonstrations with gold label-induced reasoning logic. We evaluate GPT-RE on four widely-used RE datasets, and observe that GPT-RE achieves improvements over not only existing GPT-3 baselines, but also fully-supervised baselines. Specifically, GPT-RE achieves SOTA performances on the Semeval and SciERC datasets, and competitive performances on the TACRED and ACE05 datasets.

翻译：尽管大语言模型（LLMs，如GPT-3）具有实现突破性成果的潜力，但在关系抽取（RE）任务中，它们仍明显落后于全监督基线方法（如微调后的BERT）。这主要源于LLMs在RE中的两大缺陷：（1）用于上下文学习的检索示例中，实体和关系与目标的相关性较低；（2）存在将NULL示例错误归类为其他预定义标签的强烈倾向。本文提出GPT-RE以弥合LLMs与全监督基线方法之间的差距。GPT-RE通过以下方法成功解决了上述问题：（1）在示例检索中引入任务特定的实体表示；（2）利用真实标签驱动的推理逻辑丰富示例内容。我们在四个广泛使用的RE数据集上评估GPT-RE，发现其不仅优于现有GPT-3基线方法，还超越了全监督基线方法。具体而言，GPT-RE在Semeval和SciERC数据集上取得最优性能（SOTA），并在TACRED和ACE05数据集上展现出具有竞争力的表现。