Exploiting instance-label dynamics through reciprocal anchored contrastive learning for few-shot relation extraction.

In the domain of Few-shot Relation Extraction (FSRE), the primary objective is to distill relational facts from limited labeled datasets. This task has recently witnessed significant advancements through the integration of Pre-trained Language Models (PLMs) within a supervised contrastive learning schema, which effectively leverages the dynamics between instance and label information. Despite these advancements, the comprehensive utilization of extensive instance-label pairs, aimed at facilitating the extraction of semantically rich representations within this paradigm, has yet to be fully harnessed.