Semantic Library Adaptation (SemLA) is a training-free, test-time domain adaptation framework that dynamically retrieves and merges the most relevant LoRA adapters from a library based on semantic similarity to the target domain. SemLA constructs tailored models for each input without additional training, offering scalability, explainability, and privacy preservation. See the paper for more details.
Code: https://github.com/rezaqorbani/SemLA
Project page: https://thegoodailab.org/semla
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