2 citations · 8 across the 18 of their papers we have counts for
5 papers · 2 filters
Chain-of-Thought Embeddings for Stance Detection on Social Media
Joseph Gatto, Omar Sharif, Sarah Masud Preum
Stance detection on social media is challenging for Large Language Models (LLMs), as emerging slang and colloquial language in online conversations often contain deeply implicit st…
Not Enough Labeled Data? Just Add Semantics: A Data-Efficient Method for Inferring Online Health Texts
Joseph Gatto, Sarah M. Preum
User-generated texts available on the web and social platforms are often long and semantically challenging, making them difficult to annotate. Obtaining human annotation becomes in…
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…
Theme-driven Keyphrase Extraction to Analyze Social Media Discourse
William Romano, Omar Sharif, Madhusudan Basak +2
Social media platforms are vital resources for sharing self-reported health experiences, offering rich data on various health topics. Despite advancements in Natural Language Proce…