Title: SEMANTIC-GUIDED LORA PARAMETERS GENERATION
Primary area: generative models
Keywords: neural network parameters generation, low-rank adaptation, open-world adaptation

Abstract: Generating new Low-Rank Adaptation (LoRA) weights from pre-trained LoRAs has demonstrated strong generalization capabilities across a variety of tasks for efficiently transferring AI models, especially on resource-constrained edges. However, previous studies either merge base LoRAs via weighting coefficients or train a generative model in the closed-world assumption, limiting their efficiency and flexibility in complex edge user cases. This challenge may further increase when there are significant domain shifts between training and deployment. To this end, we propose Semantic-guided LoRA Parameter Generation (SG-LoRA), a tuning-free generative framework to efficiently produce task-specific parameters for unseen tasks in a semantic-to-LoRA pipeline. Concretely, SG-LoRA uses task descriptions as the semantic bridge, measuring their proximity to a set of known expert tasks in a shared embedding space. Based on this semantic guidance, it models the target task's LoRA parameter distribution to generate high-performing parameters for novel tasks. SG-LoRA enables the real-time construction of LoRA models aligned with individual intents by distilling knowledge from prominent LoRA experts and, meanwhile, offering a privacy-preserving solution for personalized model adaptation in a novel zero-shot open-world setting proposed in this work. Extensive experiments on multiple challenging tasks confirm the superior performance and remarkable adaptability of SG-LoRA.

TL;DR: SG-LoRA