Open Information Extraction (OIE) task aims at extracting structured facts
from unstructured text, typically in the form of (subject, relation, object)
triples. Despite the potential of large language models (LLMs) like ChatGPT as
a general task solver, they lag behind state-of-the-art (supervised) methods in
OIE tasks due to two key issues. First, LLMs struggle to distinguish irrelevant
context from relevant relations and generate structured output due to the
restrictions on fine-tuning the model. Second, LLMs generates responses
autoregressively based on probability, which makes the predicted relations lack
confidence. In this paper, we assess the capabilities of LLMs in improving the
OIE task. Particularly, we propose various in-context learning strategies to
enhance LLM's instruction-following ability and a demonstration uncertainty
quantification module to enhance the confidence of the generated relations. Our
experiments on three OIE benchmark datasets show that our approach holds its
own against established supervised methods, both quantitatively and
qualitatively.

通过提出各种上下文学习策略来增强大型语言模型的指示遵循能力，以及通过演示不确定性量化模块来增强生成关系的置信度，本论文评估了大型语言模型在改进开放信息抽取任务中的能力，并在三个开放信息抽取基准数据集上进行实验证明了我们的方法在数量和质量上能够与已建立的监督方法媲美。