1 citations · 2 across the 7 of their papers we have counts for
4 papers · 1 filter
Towards Continual Learning: Mitigating Forgetting With Subspace Tuning
Arnav Chavan, Kusha Sareen, Rishabh Tiwari +6
Large language models (LLMs) acquire new skills through fine-tuning, but it can destroy capabilities they already have: catastrophic forgetting remains a central obstacle to contin…
Distill to Detect: Exposing Stealth Biases in LLMs through Cartridge Distillation
Shayan Talaei, Abhinav Chinta, Devvrit Khatri +3
Language models deployed in high-stakes roles can potentially favor certain entities, brands, or viewpoints, steering user decisions at scale. Such preferential biases can be intro…
Learning, Fast and Slow: Towards LLMs That Adapt Continually
Rishabh Tiwari, Kusha Sareen, Lakshya A Agrawal +6
Large language models (LLMs) are trained for downstream tasks by updating their parameters (e.g., via RL). However, updating model parameters forces them to absorb task-specific in…
Interleaved Head Attention
Sai Surya Duvvuri, Chanakya Ekbote, Rachit Bansal +6
Multi-Head Attention (MHA) is the core computational primitive underlying modern Large Language Models (LLMs). However, MHA suffers from a fundamental linear scaling limitation: $H…