4 papers
Exploring Continual Fine-Tuning for Enhancing Language Ability in Large Language Model
Divyanshu Aggarwal, Sankarshan Damle, Navin Goyal +2
A common challenge towards the adaptability of Large Language Models (LLMs) is their ability to learn new languages over time without hampering the model's performance on languages…
SLIP: Securing LLMs IP Using Weights Decomposition
Yehonathan Refael, Adam Hakim, Lev Greenberg +6
Large language models (LLMs) have recently seen widespread adoption in both academia and industry. As these models grow, they become valuable intellectual property (IP), reflecting…
SLIP-SEC: Formalizing Secure Protocols for Model IP Protection
Racchit Jain, Satya Lokam, Yehonathan Refael +3
Large Language Models (LLMs) represent valuable intellectual property (IP), reflecting significant investments in training data, compute, and expertise. Deploying these models on p…
CurLL: A Developmental Framework to Evaluate Continual Learning in Language Models
Pavan Kalyan, Shubhra Mishra, Satya Lokam +1
We introduce a comprehensive continual learning dataset and benchmark (CurlL) grounded in human developmental trajectories from ages 5-10, enabling systematic and fine-grained asse…