2 citations · 6 across the 15 of their papers we have counts for
15 papers
Measuring Distribution Shift in User Prompts and Its Effects on LLM Performance
Parker Seegmiller, Sarah Masud Preum
LLMs are increasingly deployed in dynamic, real-world settings, where the distribution of user prompts can shift substantially over time as new tasks, prompts, and users are introd…
Medical Triage as Pairwise Ranking: A Benchmark for Urgency in Patient Portal Messages
Joseph Gatto, Parker Seegmiller, Timothy Burdick +4
Medical triage is the task of allocating medical resources and prioritizing patients based on medical need. This paper introduces the first large-scale public dataset for studying…
How Much Would a Clinician Edit This Draft? Evaluating LLM Alignment for Patient Message Response Drafting
Parker Seegmiller, Joseph Gatto, Sarah E. Greer +4
Large language models (LLMs) show promise in drafting responses to patient portal messages, yet their integration into clinical workflows raises various concerns, including whether…
FLAMES: Improving LLM Math Reasoning via a Fine-Grained Analysis of the Data Synthesis Pipeline
Parker Seegmiller, Kartik Mehta, Soumya Saha +6
Recent works improving LLM math reasoning with synthetic data have used unique setups, making comparison of data synthesis strategies impractical. This leaves many unanswered quest…
The Amazon Nova Family of Models: Technical Report and Model Card
Amazon AGI, Aaron Langford, Aayush Shah +783
We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…
Follow-up Question Generation For Enhanced Patient-Provider Conversations
Joseph Gatto, Parker Seegmiller, Timothy Burdick +3
Follow-up question generation is an essential feature of dialogue systems as it can reduce conversational ambiguity and enhance modeling complex interactions. Conversational contex…
In-Context Learning for Preserving Patient Privacy: A Framework for Synthesizing Realistic Patient Portal Messages
Joseph Gatto, Parker Seegmiller, Timothy E. Burdick +1
Since the COVID-19 pandemic, clinicians have seen a large and sustained influx in patient portal messages, significantly contributing to clinician burnout. To the best of our knowl…
Depth : Improving Evaluation of Cross-Domain Text Classification by Measuring Semantic Generalizability
Parker Seegmiller, Joseph Gatto, Sarah Masud Preum
Recent evaluations of cross-domain text classification models aim to measure the ability of a model to obtain domain-invariant performance in a target domain given labeled samples…
Do LLMs Find Human Answers To Fact-Driven Questions Perplexing? A Case Study on Reddit
Parker Seegmiller, Joseph Gatto, Omar Sharif +2
Large language models (LLMs) have been shown to be proficient in correctly answering questions in the context of online discourse. However, the study of using LLMs to model human-l…
Large Language Models for Document-Level Event-Argument Data Augmentation for Challenging Role Types
Joseph Gatto, Parker Seegmiller, Omar Sharif +1
Event Argument Extraction (EAE) is an extremely difficult information extraction problem -- with significant limitations in few-shot cross-domain (FSCD) settings. A common solution…
Statistical Depth for Ranking and Characterizing Transformer-Based Text Embeddings
Parker Seegmiller, Sarah Masud Preum
The popularity of transformer-based text embeddings calls for better statistical tools for measuring distributions of such embeddings. One such tool would be a method for ranking t…
Text Encoders Lack Knowledge: Leveraging Generative LLMs for Domain-Specific Semantic Textual Similarity
Joseph Gatto, Omar Sharif, Parker Seegmiller +2
Amidst the sharp rise in the evaluation of large language models (LLMs) on various tasks, we find that semantic textual similarity (STS) has been under-explored. In this study, we…
The Scope of In-Context Learning for the Extraction of Medical Temporal Constraints
Parker Seegmiller, Joseph Gatto, Madhusudan Basak +4
Medications often impose temporal constraints on everyday patient activity. Violations of such medical temporal constraints (MTCs) lead to a lack of treatment adherence, in additio…
ActSafe: Predicting Violations of Medical Temporal Constraints for Medication Adherence
Parker Seegmiller, Joseph Gatto, Abdullah Mamun +4
Prescription medications often impose temporal constraints on regular health behaviors (RHBs) of patients, e.g., eating before taking medication. Violations of such medical tempora…
HealthE: Classifying Entities in Online Textual Health Advice
Joseph Gatto, Parker Seegmiller, Garrett Johnston +1
The processing of entities in natural language is essential to many medical NLP systems. Unfortunately, existing datasets vastly under-represent the entities required to model publ…