To address catastrophic forgetting in Continual Relation Extraction (CRE), many current approaches rely on memory buffers to rehearse previously learned knowledge while acquiring new tasks. Recently, prompt-based methods have emerged as potent alternatives to rehearsal-based strategies, demonstrating strong empirical performance. However, upon analyzing existing prompt-based approaches for CRE, we identified several critical limitations, such as inaccurate prompt selection, inadequate mechanisms for mitigating forgetting in shared parameters, and suboptimal handling of cross-task and within-task variances. To overcome these challenges, we draw inspiration from the relationship between prefix-tuning and mixture of experts, proposing a novel approach that employs a prompt pool for each task, capturing variations within each task while enhancing cross-task variances. Furthermore, we incorporate a generative model to consolidate prior knowledge within shared parameters, eliminating the need for explicit data storage. Extensive experiments validate the efficacy of our approach, demonstrating superior performance over state-of-the-art prompt-based and rehearsal-free methods in continual relation extraction.
@article{arxiv.2412.08285,
title = {Adaptive Prompting for Continual Relation Extraction: A Within-Task Variance Perspective},
author = {Minh Le and Tien Ngoc Luu and An Nguyen The and Thanh-Thien Le and Trang Nguyen and Tung Thanh Nguyen and Linh Ngo Van and Thien Huu Nguyen},
journal= {arXiv preprint arXiv:2412.08285},
year = {2025}
}