In-Context Few-Shot Relation Extraction via Pre-Trained Language Models

Relation extraction aims at inferring structured human knowledge from textual documents. State-of-the-art methods based on language models commonly have two limitations: (1) they require named entities to be either given as input or infer them, which introduces additional noise, and (2) they require human annotations of documents. As a remedy, we present a novel framework for in-context few-shot relation extraction via pre-trained language models. To the best of our knowledge, we are the first to reformulate the relation extraction task as a tailored in-context few-shot learning paradigm. Thereby, we achieve crucial benefits in that we eliminate the need for both named entity recognition and human annotation of documents. Unlike existing methods based on fine-tuning, our framework is flexible in that it can be easily updated for a new set of relations without re-training. We evaluate our framework using DocRED, the largest publicly available dataset for document-level relation extraction, and demonstrate that our framework achieves state-of-the-art performance. Finally, our framework allows us to identify missing annotations, and we thus show that our framework actually performs much better than the original labels from the development set of DocRED.