C-ICL: Contrastive In-context Learning for Information Extraction
Ying Mo Jian Yang Jiahao Liu Shun Zhang Jingang Wang Zhoujun Li
Recently, there has been increasing interest in exploring the capabilities of
advanced large language models (LLMs) in the field of information extraction
(IE), specifically focusing on tasks related to named entity recognition (NER)
and relation extraction (RE). Although researchers are exploring the use of
few-shot information extraction through in-context learning with LLMs, they
tend to focus only on using correct or positive examples for demonstration,
neglecting the potential value of incorporating incorrect or negative examples
into the learning process. In this paper, we present c-ICL, a novel few-shot
technique that leverages both correct and incorrect sample constructions to
create in-context learning demonstrations. This approach enhances the ability
of LLMs to extract entities and relations by utilizing prompts that incorporate
not only the positive samples but also the reasoning behind them. This method
allows for the identification and correction of potential interface errors.
Specifically, our proposed method taps into the inherent contextual information
and valuable information in hard negative samples and the nearest positive
neighbors to the test and then applies the in-context learning demonstrations
based on LLMs. Our experiments on various datasets indicate that c-ICL
outperforms previous few-shot in-context learning methods, delivering
substantial enhancements in performance across a broad spectrum of related
tasks. These improvements are noteworthy, showcasing the versatility of our
approach in miscellaneous scenarios.