Papers by Sahar Rajabi
GenKnowSub: Improving Modularity and Reusability of LLMs through General Knowledge Subtraction (2025.acl-short)
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| Challenge: | Large language models (LLMs) struggle with zero-shot generalization due to entanglement of general knowledge and task-specific adaptations. |
| Approach: | They propose a modular framework that disentangles general knowledge and adaptations by constructing a library of task-specific LoRA modules alongside a general-domain LoRA. |
| Outcome: | The proposed framework disentangles general knowledge and task-specific adaptations . it generates residual modules that focus more exclusively on task-relevant information . |