3 citations · 5 across the 12 of their papers we have counts for
6 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…
APEX: Automated Prompt Engineering eXpert with Dynamic Data Selection
Fei Wang, Si Si, Cho-Jui Hsieh +1
Large Language Models are highly sensitive to prompt formulation, necessitating automatic prompt optimization to unlock their full potential. While evolutionary algorithms have eme…
ODRPO: Ordinal Decompositions of Discrete Rewards for Robust Policy Optimization
Nirmal Patel, Fei Wang, Inderjit S. Dhillon
The alignment of Large Language Models (LLMs) utilizes Reinforcement Learning from AI Feedback (RLAIF) for non-verifiable domains such as long-form question answering and open-ende…
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…
LUCID: Attention with Preconditioned Representations
Sai Surya Duvvuri, Nirmal Patel, Nilesh Gupta +1
Softmax-based dot-product attention is a cornerstone of Transformer architectures, enabling remarkable capabilities such as in-context learning. However, as context lengths increas…