Few-Shot, No Problem: Descriptive Continual Relation Extraction
This addresses the problem of catastrophic forgetting in dynamic real-world domains for AI systems, representing a novel method for a known bottleneck.
The paper tackles the challenge of few-shot continual relation extraction, where AI systems must adapt to evolving relationships with limited data, by proposing a retrieval-based method that uses large language models to generate relation descriptions and achieves state-of-the-art performance in experiments across multiple datasets.
Few-shot Continual Relation Extraction is a crucial challenge for enabling AI systems to identify and adapt to evolving relationships in dynamic real-world domains. Traditional memory-based approaches often overfit to limited samples, failing to reinforce old knowledge, with the scarcity of data in few-shot scenarios further exacerbating these issues by hindering effective data augmentation in the latent space. In this paper, we propose a novel retrieval-based solution, starting with a large language model to generate descriptions for each relation. From these descriptions, we introduce a bi-encoder retrieval training paradigm to enrich both sample and class representation learning. Leveraging these enhanced representations, we design a retrieval-based prediction method where each sample "retrieves" the best fitting relation via a reciprocal rank fusion score that integrates both relation description vectors and class prototypes. Extensive experiments on multiple datasets demonstrate that our method significantly advances the state-of-the-art by maintaining robust performance across sequential tasks, effectively addressing catastrophic forgetting.