13 citations · 32 across the 10 of their papers we have counts for
5 papers · 1 filter
Extrinsically-Focused Evaluation of Omissions in Medical Summarization
Elliot Schumacher, Daniel Rosenthal, Dhruv Naik +4
Large language models (LLMs) have shown promise in safety-critical applications such as healthcare, yet the ability to quantify performance has lagged. An example of this challenge…
Generating medically-accurate summaries of patient-provider dialogue: A multi-stage approach using large language models
Varun Nair, Elliot Schumacher, Anitha Kannan
A medical provider's summary of a patient visit serves several critical purposes, including clinical decision-making, facilitating hand-offs between providers, and as a reference f…
CONSCENDI: A Contrastive and Scenario-Guided Distillation Approach to Guardrail Models for Virtual Assistants
Albert Yu Sun, Varun Nair, Elliot Schumacher +1
A wave of new task-based virtual assistants has been fueled by increasingly powerful large language models (LLMs), such as GPT-4 (OpenAI, 2023). A major challenge in deploying LLM-…
DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents
Varun Nair, Elliot Schumacher, Geoffrey Tso +1
Large language models (LLMs) have emerged as valuable tools for many natural language understanding tasks. In safety-critical applications such as healthcare, the utility of these…
Adding more data does not always help: A study in medical conversation summarization with PEGASUS
Varun Nair, Namit Katariya, Xavier Amatriain +2
Medical conversation summarization is integral in capturing information gathered during interactions between patients and physicians. Summarized conversations are used to facilitat…