3 citations · 3 across the 2 of their papers we have counts for
4 papers
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…
SQuat: Subspace-orthogonal KV Cache Quantization
Hao Wang, Ligong Han, Kai Xu +1
The key-value (KV) cache accelerates LLMs decoding by storing KV tensors from previously generated tokens. It reduces redundant computation at the cost of increased memory usage. T…
Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods
Isha Puri, Shivchander Sudalairaj, Guangxuan Xu +2
Large language models (LLMs) have achieved significant performance gains via scaling up model sizes and/or data. However, recent evidence suggests diminishing returns from such app…
Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs
Aldo Pareja, Nikhil Shivakumar Nayak, Hao Wang +10
The rise of large language models (LLMs) has created a significant disparity: industrial research labs with their computational resources, expert teams, and advanced infrastructure…