Test:2023 ZeroShotInformationExtractionvi

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Subject Headings: Llm-Based Information Extraction, Zero-Shot Information Extraction (IE).

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Abstract

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.

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2023 ZeroShotInformationExtractionviXiang Wei
Xingyu Cui
Ning Cheng
Xiaobin Wang
Xin Zhang
Shen Huang
Pengjun Xie
Jinan Xu
Yufeng Chen
Meishan Zhang
Zero-shot Information Extraction via Chatting with Chatgpt10.48550/arXiv.2302.10205