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cs.AI2025
A Library of LLM Intrinsics for Retrieval-Augmented Generation
Marina Danilevsky, Kristjan Greenewald, Chulaka Gunasekara +13
In the developer community for large language models (LLMs), there is not yet a clean pattern analogous to a software library, to support very large scale collaboration. Even for t…
cs.LG2025
Sculpting Subspaces: Constrained Full Fine-Tuning in LLMs for Continual Learning
Nikhil Shivakumar Nayak, Krishnateja Killamsetty, Ligong Han +8
Continual learning in large language models (LLMs) is prone to catastrophic forgetting, where adapting to new tasks significantly degrades performance on previously learned ones. E…
cs.CL2025
DELIFT: Data Efficient Language model Instruction Fine Tuning
Ishika Agarwal, Krishnateja Killamsetty, Lucian Popa +1
Fine-tuning large language models (LLMs) is essential for enhancing their performance on specific tasks but is often resource-intensive due to redundant or uninformative data. To a…