UNIT：释放大语言模型在图连续学习中的潜力
UNIT: Unleash Large Language Models Potential for Graph Continual Learning
- Central South University（中南大学）
- Hongkong University of Science and Technology (Guangzhou)（香港科技大学（广州））
- Hunan Institute of Engineering（湖南工程学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对图连续学习面临的语义-结构分离和知识转移不平衡问题，提出UNIT框架，通过微调大语言模型、引入不确定感知锚点生成机制和结构融合建模，提升模型适应性与跨任务知识保留能力，在图连续学习任务中达最优性能。
AI中文摘要：
在现实世界的多模态网络场景中，图结构数据常以流方式到达，使图连续学习成为对这类不断演变结构进行持续建模的关键范式。现有图连续学习方法面临语义-结构分离及知识转移不平衡两大挑战。为此提出UNIT框架，通过仅在首个任务上微调大语言模型，弥合预训练大语言模型语料库与目标任务数据集的分布差距，提升其对图结构任务的适应性。同时提出不确定感知锚点生成机制，有效保留跨任务的代表性知识。还引入结构融合建模将图拓扑信息融入语义信息。实验表明该方法在图连续学习任务中取得了最优性能。
英文摘要：
In real-world multimodal web scenarios, graph-structured data often arrives in a streaming manner, making graph continual learning a crucial paradigm for continuously modeling such evolving structures. However, existing graph continual learning methods still face two fundamental challenges. 1) semantic-structural separation, where the graph-based methods excel at modeling topological relationships but neglect deep semantics. 2) imbalanced knowledge transfer, where existing models fail to effectively leverage general knowledge gained from early tasks to benefit subsequent new tasks. To address above issues, we propose a novel framework, \textbf{UN}leash Large Language Models PotentIal for Graph ConTinual Learning (UNIT). By fine-tuning large language model only on the first task, we bridge the distributional gap between the pre-trained LLM corpus and the target task dataset to enhance the adaptability of LLMs for graph-structured tasks. Meanwhile, we propose an uncertain-aware anchor generation mechanism to effectively preserve representative knowledge across tasks, avoiding the neglect of universal knowledge learned from previous tasks. Additionally, we introduce structural confluence modeling to explicitly integrates graph topology information into semantic information, enhancing the collaborative capabilities between semantic understanding and structural modeling. Extensive experiments demonstrate that our proposed method achieves state-of-the-art performance in the graph continual learning task.
