利用被忽略的模型头提升少样本持续关系抽取的泛化能力
Preserving Generalization of Language models in Few-shot Continual Relation Extraction
- 复用被丢弃的语言模型头，通过互信息最大化策略保留先验知识
- 在多个基准数据集上显著缓解灾难性遗忘，准确率提升超10%
- 适合研究持续学习与大模型应用的科研人员参考
少样本持续关系抽取（FCRE）是新兴且动态的研究方向，要求模型在仅用少量标注数据的情况下，顺序学习新关系，同时避免灾难性遗忘并保留预训练主干模型的知识。本文提出一种新方法，利用通常被丢弃的语言模型头，通过互信息最大化策略，帮助维持预训练主干的先验知识，并战略性对齐主分类头，从而提升模型性能。此外，我们探索了大语言模型（LLMs）在解决FCRE挑战中的潜力。全面的实验结果验证了该方法的有效性，并为未来工作提供了重要启示。
原文摘要 · Abstract (English)
Few-shot Continual Relations Extraction (FCRE) is an emerging and dynamic area of study where models can sequentially integrate knowledge from new relations with limited labeled data while circumventing catastrophic forgetting and preserving prior knowledge from pre-trained backbones. In this work, we introduce a novel method that leverages often-discarded language model heads. By employing these components via a mutual information maximization strategy, our approach helps maintain prior knowledge from the pre-trained backbone and strategically aligns the primary classification head, thereby enhancing model performance. Furthermore, we explore the potential of Large Language Models (LLMs), renowned for their wealth of knowledge, in addressing FCRE challenges. Our comprehensive experimental results underscore the efficacy of the proposed method and offer valuable insights for future work.
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