用知识增强时序适应，让模型更好应对未来数据变化。
Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation
- 引入医学术语本体等知识源，动态捕捉语义与知识演变
- 在医疗、法律、科学多领域任务中实现稳定性能提升
- 适合需要长期部署的时序建模场景，如智能诊断
时间带来模型开发与部署的根本挑战：模型通常基于历史数据训练，但部署于未来数据，其语义分布和领域知识可能已演变。现有研究或忽略时序偏移，或难以捕捉语义与知识的复杂变化。我们提出知识驱动的增强与检索集成时序适应方法（KARITA），以捕获多样化的时序偏移（如不确定性与特征漂移），构建并整合丰富知识源（如医学术语本体MeSH），并利用动态演化洞察进行选择性检索与增强学习。在医疗、法律、科学等多个领域的分类任务上评估KARITA，结果表明其在多领域均实现一致性能提升。实验显示，知识融合在时序增强与学习中更具关键性和有效性。
原文摘要 · Abstract (English)
Time introduces fundamental challenges in model development and deployment: models are usually trained on historical data while deployed on future data where semantic distributions and domain knowledge may evolve. Unfortunately, existing studies either overlook temporal shifts or hardly capture rich shifting patterns of both semantic and knowledge. We develop Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation (KARITA) to capture diverse temporal shifts (e.g., uncertainty and feature shift), construct and integrate rich knowledge sources (e.g., medical ontology like MeSH), and leverage shifting insights for selecting-retrieval augmented learning. We evaluate KARITA on classification tasks across multiple domains, clinical, legal, and scientific corpora, demonstrating consistent improvements across multiple domains with temporal adaptation. Our results show that knowledge integration can be more critical and effective in temporal augmentation and learning.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。