5 papers
Reflections and New Directions for Human-Centered Large Language Models
Caleb Ziems, Dora Zhao, Rose E. Wang +55
Large Language Models (LLMs) are increasingly shaping the private and professional lives of users, with numerous applications in business, education, finance, healthcare, law, and…
CausalSent: Interpretable Sentiment Classification with RieszNet
Daniel Frees, Martin Pollack
Despite the overwhelming performance improvements offered by recent natural language processing (NLP) models, the decisions made by these models are largely a black box. Towards cl…
Towards Optimal Convolutional Transfer Learning Architectures for Breast Lesion Classification and ACL Tear Detection
Daniel Frees, Moritz Bolling, Aditri Bhagirath
Modern computer vision models have proven to be highly useful for medical imaging classification and segmentation tasks, but the scarcity of medical imaging data often limits the e…
Exploring Efficient Learning of Small BERT Networks with LoRA and DoRA
Daniel Frees, Aditri Bhagirath, Moritz Bolling
While Large Language Models (LLMs) have revolutionized artificial intelligence, fine-tuning LLMs is extraordinarily computationally expensive, preventing smaller businesses and res…
SynthesizeMe! Inducing Persona-Guided Prompts for Personalized Reward Models in LLMs
Michael J Ryan, Omar Shaikh, Aditri Bhagirath +3
Recent calls for pluralistic alignment of Large Language Models (LLMs) encourage adapting models to diverse user preferences. However, most prior work on personalized reward models…