5 citations · 13 across the 14 of their papers we have counts for
6 papers · 1 filter
Fair In-Context Learning via Latent Concept Variables
Karuna Bhaila, Minh-Hao Van, Kennedy Edemacu +3
The emerging in-context learning (ICL) ability of large language models (LLMs) has prompted their use for predictive tasks in various domains with different data types, including t…
Soft Prompting for Unlearning in Large Language Models
Karuna Bhaila, Minh-Hao Van, Xintao Wu
The widespread popularity of Large Language Models (LLMs), partly due to their unique ability to perform in-context learning, has also brought to light the importance of ethical an…
Beyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis
Prateek Verma, Minh-Hao Van, Xintao Wu
Vision language models (VLMs) have recently emerged and gained the spotlight for their ability to comprehend the dual modality of image and textual data. VLMs such as LLaVA, ChatGP…
Robust Influence-based Training Methods for Noisy Brain MRI
Minh-Hao Van, Alycia N. Carey, Xintao Wu
Correctly classifying brain tumors is imperative to the prompt and accurate treatment of a patient. While several classification algorithms based on classical image processing or d…
On Large Visual Language Models for Medical Imaging Analysis: An Empirical Study
Minh-Hao Van, Prateek Verma, Xintao Wu
Recently, large language models (LLMs) have taken the spotlight in natural language processing. Further, integrating LLMs with vision enables the users to explore emergent abilitie…
In-Context Learning Demonstration Selection via Influence Analysis
Vinay M. S., Minh-Hao Van, Xintao Wu
Large Language Models (LLMs) have showcased their In-Context Learning (ICL) capabilities, enabling few-shot learning without the need for gradient updates. Despite its advantages,…