Challenges in integrating LLMs with graph learning
It’s worth noting that integrating LLMs with graph learning involves several challenges:
- Efficiency and scalability: LLMs require significant computational resources, which poses deployment challenges in real-world applications, particularly on resource-constrained devices. Knowledge distillation, where an LLM (teacher model) transfers knowledge to a smaller, efficient GNN (student model), offers a promising solution.
- Data leakage and evaluation: LLMs pre-trained on vast datasets risk data leakage, potentially inflating performance metrics. Mitigating this requires new datasets and careful test data sampling. Establishing fair evaluation benchmarks is also crucial for accurate performance assessment.
- Transferability and explainability: Enhancing LLMs’ ability to transfer knowledge across diverse graph domains and improving their explainability is vital. Techniques such as chain-of-thought prompting can...
