4 papers · 1 filter
RAPTOR: Role-Aware Private Training for Mixture-of-Experts
Duc Dm, Khai Le-Duc, Nguyen Do +18
Differentially private (DP) fine-tuning methods treat sparse Mixture-of-Experts (MoE) models as a single dense block, ignoring that shared layers see all data while experts only se…
Selective Steering: Norm-Preserving Control Through Discriminative Layer Selection
Quy-Anh Dang, Chris Ngo
Despite significant progress in alignment, large language models (LLMs) remain vulnerable to adversarial attacks that elicit harmful behaviors. Activation steering techniques offer…
Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't
Quy-Anh Dang, Chris Ngo
Enhancing the reasoning capabilities of large language models (LLMs) typically relies on massive computational resources and extensive datasets, limiting accessibility for resource…
MoD: A Distribution-Based Approach for Merging Large Language Models
Quy-Anh Dang, Chris Ngo
Large language models (LLMs) have enabled the development of numerous specialized, task-specific variants. However, the maintenance and deployment of these individual models presen…