4 papers
Grow, Don't Overwrite: Fine-tuning Without Forgetting
Dyah Adila, Hanna Mazzawi, Benoit Dherin +1
Adapting pre-trained models to specialized tasks often leads to catastrophic forgetting, where new knowledge overwrites foundational capabilities. Existing methods either compromis…
Weight Updates as Activation Shifts: A Principled Framework for Steering
Dyah Adila, John Cooper, Alexander Yun +2
Activation steering promises to be an extremely parameter-efficient form of adaptation, but its effectiveness depends on critical design choices -- such as intervention location an…
CrEst: Credibility Estimation for Contexts in LLMs via Weak Supervision
Dyah Adila, Shuai Zhang, Boran Han +2
The integration of contextual information has significantly enhanced the performance of large language models (LLMs) on knowledge-intensive tasks. However, existing methods often o…
Personalize Your LLM: Fake it then Align it
Yijing Zhang, Dyah Adila, Changho Shin +1
Personalizing large language models (LLMs) is essential for delivering tailored interactions that improve user experience. Many existing personalization methods require fine-tuning…