Xiaohui Huo

Representation-Aware Modularity: Efficient Cross-Task Generalization for LLMs

Cross-task generalization (CTG) enables large language models (LLMs) to handle unseen tasks proficiently, enhancing their adaptability in real-world scenarios. However, existing methods relying on per-token dynamic routing to multiple trained LoRA adapters face high computational and GPU memory cost