8 citations · 17 across the 8 of their papers we have counts for
3 papers · 1 filter
Robust and Efficient Fine-tuning of LLMs with Bayesian Reparameterization of Low-Rank Adaptation
Ayan Sengupta, Vaibhav Seth, Arinjay Pathak +5
Large Language Models (LLMs) are highly resource-intensive to fine-tune due to their enormous size. While low-rank adaptation is a prominent parameter-efficient fine-tuning approac…
Synthetic Data Applications in Finance
Vamsi K. Potluru, Daniel Borrajo, Andrea Coletta +17
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality. We present a broad overview of prototypical applicatio…
Let Me At Least Learn What You Really Like: Dealing With Noisy Humans When Learning Preferences
Sriram Gopalakrishnan, Utkarsh Soni
Learning the preferences of a human improves the quality of the interaction with the human. The number of queries available to learn preferences maybe limited especially when inter…