GenKnowSub: Improving Modularity and Reusability of LLMs through General Knowledge Subtraction

Large language models often struggle with zero-shot generalization, and several modular approaches have been proposed to address this challenge.Yet, we hypothesize that a key limitation remains: the entanglement of general knowledge and task-specific adaptations.To overcome this, we propose a modular framework that disentangles these components by constructing a library of task-specific LoRA modules alongside a general-domain LoRA.By subtracting this general knowledge component from each task-specific module, we obtain residual modules that focus more exclusively on task-relevant information-a method we call general knowledge subtraction (GenKnowSub).Leveraging the refined task-specific modules and the Arrow routing algorithm (Ostapenko et al., 2024), we dynamically select and combine modules for new inputs without additional training.Our studies on the Phi-3 model and standard Arrow as baselines reveal that using general knowledge LoRAs derived from diverse languages, including English, French, and German, yields consistent performance gains in both monolingual and cross-lingual settings across a wide set of benchmarks.Further experiments on Phi-2 demonstrate how Gen-KnowSub generalizes to weaker LLMs.