2 citations · 6 across the 15 of their papers we have counts for
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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…