Open Information Extraction (OIE) aims to extract objective structured
knowledge from natural texts, which has attracted growing attention to build
dedicated models with human experience. As the large language models (LLMs)
have exhibited remarkable in-context learning capabilities, a question arises
as to whether the task of OIE can be effectively tackled with this paradigm? In
this paper, we explore solving the OIE problem by constructing an appropriate
reasoning environment for LLMs. Specifically, we first propose a method to
effectively estimate the discrepancy of syntactic distribution between a LLM
and test samples, which can serve as correlation evidence for preparing
positive demonstrations. Upon the evidence, we introduce a simple yet effective
mechanism to establish the reasoning environment for LLMs on specific tasks.
Without bells and whistles, experimental results on the standard CaRB benchmark
demonstrate that our $6$-shot approach outperforms state-of-the-art supervised
method, achieving an $55.3$ $F_1$ score. Further experiments on TACRED and
ACE05 show that our method can naturally generalize to other information
extraction tasks, resulting in improvements of $5.7$ and $6.8$ $F_1$ scores,
respectively.

通过构建合适的推理环境，本文探索利用大型语言模型（LLMs）解决开放式信息抽取（OIE）问题，实验结果表明，相较于最先进的监督方法，我们的 6-shot 方法在标准 CaRB 基准上实现了 55.3 的 F1 分数，同时在 TACRED 和 ACE05 上实验结果显示，我们的方法可以自然地推广到其他信息抽取任务，并分别获得了 5.7 和 6.8 的 F1 分数提升。