Zero-shot information extraction (IE) aims to build IE systems from the unannotated text. It is challenging due to involving little human intervention. Challenging but worthwhile, zero-shot IE reduces the time and effort that data labeling takes. Recent efforts on large language models (LLMs, e.g., GPT-3, ChatGPT) show promising performance on zero-shot settings, thus inspiring us to explore prompt-based methods. In this work, we ask whether strong IE models can be constructed by directly prompting LLMs. Specifically, we transform the zero-shot IE task into a multi-turn question-answering problem with a two-stage framework (ChatIE). With the power of ChatGPT, we extensively evaluate our framework on three IE tasks: entity-relation triple extract, named entity recognition, and event extraction. Empirical results on six datasets across two languages show that ChatIE achieves impressive performance and even surpasses some full-shot models on several datasets (e.g., NYT11-HRL). We believe that our work could shed light on building IE models with limited resources.

翻译：零样本信息抽取（IE）旨在从未标注文本中构建信息抽取系统。由于涉及较少的人工干预，这一任务颇具挑战性。尽管困难重重但意义重大，零样本信息抽取能显著减少数据标注所需的时间和精力。近期针对大型语言模型（例如GPT-3、ChatGPT）的研究在零样本设置中展现出令人瞩目的性能，这激励我们探索基于提示的方法。本研究旨在探讨是否可以通过直接提示大型语言模型来构建强大的信息抽取模型。具体而言，我们通过一个两阶段框架（ChatIE）将零样本信息抽取任务转化为多轮问答问题。借助ChatGPT的强大能力，我们在三个信息抽取任务（实体关系三元组抽取、命名实体识别和事件抽取）上全面评估了该框架。在两个语言的六个数据集上的实证结果表明，ChatIE取得了令人印象深刻的性能，甚至在多个数据集（如NYT11-HRL）上超越了部分全样本模型。我们相信，本研究可为在有限资源条件下构建信息抽取模型提供新思路。