LC-ICL: Label-Guided Contrastive In-Context Learning for Robust Information Extraction
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 LC-ICL a novel few-shot technique that leverages both correct and incorrect sample constructions to create in-conte...
| Authors | Xiao You, Tianwei Yan, Shan Zhao |
|---|---|
| Primary category | cs.CL |
| Published | 28 June 2026 |
| Last updated at source | 28 June 2026 |
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