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 \citep{ostapenko2024towards}, 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 GenKnowSub generalizes to weaker LLMs. The complete code and data are available at https://github.com/saharsamr/Modular-LLM.

SubTrack your Grad: Gradient Subspace Tracking for Memory and Time Efficient Full-Parameter LLM Training

Training Large Language Models (LLMs) demand significant time and computational resources due to their large model sizes and optimizer states. To overcome these challenges, recent methods, such as BAdam, employ partial weight updates to enhance time and memory efficiency, though sometimes at the cost of performance. Others, like GaLore, focus on maintaining performance while optimizing memory usage through full parameter training, but may incur higher time complexity. By leveraging the low-rank structure of the gradient and the Grassmannian geometry, we propose SubTrack-Grad, a subspace tracking-based optimization method that efficiently tracks the evolving gradient subspace by incorporating estimation errors and previously identified subspaces. SubTrack-Grad delivers better or on-par results compared to GaLore, while significantly outperforming BAdam, which, despite being time-efficient, compromises performance. SubTrack-Grad reduces wall-time by up to 20.57% on GLUE tasks (15% average reduction) and up to 65% on SuperGLUE tasks (22% average reduction) compared to GaLore. Notably, for a 3B parameter model, GaLore incurred a substantial 157% increase in wall-time compared to full-rank training, whereas SubTrack-Grad exhibited a 31% increase, representing a 49% reduction in wall-time, while enjoying the same memory reductions as GaLore.