14 citations · 19 across the 6 of their papers we have counts for
7 papers
Towards Better Health Conversations: The Benefits of Context-seeking
Rory Sayres, Yuexing Hao, Abbi Ward +21
Navigating health questions can be daunting in the modern information landscape. Large language models (LLMs) may provide tailored, accessible information, but also risk being inac…
From Barriers to Tactics: A Behavioral Science-Informed Agentic Workflow for Personalized Nutrition Coaching
Eric Yang, Tomas Garcia, Hannah Williams +7
Effective management of cardiometabolic conditions requires sustained positive nutrition habits, often hindered by complex and individualized barriers. Direct human management is s…
The Geometry of Queries: Query-Based Innovations in Retrieval-Augmented Generation for Healthcare QA
Eric Yang, Jonathan Amar, Jong Ha Lee +2
Deploying Large Language Models (LLMs) for healthcare question answering requires robust methods to ensure accuracy and reliability. This work introduces Query-Based Retrieval Augm…
Selective Fine-tuning on LLM-labeled Data May Reduce Reliance on Human Annotation: A Case Study Using Schedule-of-Event Table Detection
Bhawesh Kumar, Jonathan Amar, Eric Yang +2
Large Language Models (LLMs) have demonstrated their efficacy across a broad spectrum of tasks in healthcare applications. However, often LLMs need to be fine-tuned on task-specifi…
Labrador: Exploring the Limits of Masked Language Modeling for Laboratory Data
David R. Bellamy, Bhawesh Kumar, Cindy Wang +1
In this work we introduce Labrador, a pre-trained Transformer model for laboratory data. Labrador and BERT were pre-trained on a corpus of 100 million lab test results from electro…
Conformal Prediction with Large Language Models for Multi-Choice Question Answering
Bhawesh Kumar, Charlie Lu, Gauri Gupta +4
As large language models continue to be widely developed, robust uncertainty quantification techniques will become crucial for their safe deployment in high-stakes scenarios. In th…
Towards Reliable Zero Shot Classification in Self-Supervised Models with Conformal Prediction
Bhawesh Kumar, Anil Palepu, Rudraksh Tuwani +1
Self-supervised models trained with a contrastive loss such as CLIP have shown to be very powerful in zero-shot classification settings. However, to be used as a zero-shot classifi…