G2LoRA: Gradient Orthogonal Low-Rank Adaptation Framework for Graph Continual Learning on Text-Attributed Graphs
arXiv:2606.01873v1 Announce Type: new Abstract: LLM-as-Aligner has emerged as a prevalent pre-training paradigm for Text-Attributed Graphs(TAGS), aligning graph and text modalities into a shared embedding space via CLIP-style contrastive learning. While effective on individual downstream tasks, we observe severe catastrophic forgetting when such models are sequentially fine-tuned on streaming tasks. Although parameter-efficient fine-tuning alleviates forgetting to some extent, it remains insufficient to resolve task interference and ineffective knowledge transfer. In this work, we study graph
The proliferation of LLMs and their application to diverse data types like Text-Attributed Graphs is creating new challenges in continual learning that require immediate attention.
Addressing catastrophic forgetting in AI models is crucial for their long-term viability and efficiency in dynamic, real-world environments, directly impacting the developmental trajectory of advanced AI systems.
This research introduces a novel framework to mitigate a significant limitation in sequential fine-tuning of LLM-aligned graph models, enabling more robust and adaptable AI for complex data structures.
- · AI researchers
- · Graph AI developers
- · Continual learning applications
- · Autonomous AI systems
- · AI models without continuous adaptation
- · Brute-force fine-tuning methods
Improved performance and stability of AI models deployed in evolving data environments.
Accelerated development and adoption of AI agents capable of continuous self-improvement and adaptation.
Enhanced reliability and trustworthiness of AI systems, potentially broadening their societal and economic integration.
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Read at arXiv cs.LG