Test:2023 ZeroShotInformationExtractionvi
- (Wei et al., 2023) ⇒ Xiang Wei, Xingyu Cui, Ning Cheng, Xiaobin Wang, Xin Zhang, Shen Huang, Pengjun Xie, Jinan Xu, Yufeng Chen, Meishan Zhang and others. (2023). “Zero-shot Information Extraction via Chatting with Chatgpt.” In: arXiv preprint arXiv:2302.10205. doi:10.48550/arXiv.2302.10205
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.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023 ZeroShotInformationExtractionvi | Xiang 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 Chatgpt | 10.48550/arXiv.2302.10205 |