1 citations · 2 across the 8 of their papers we have counts for
8 papers
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…
Scope of Large Language Models for Mining Emerging Opinions in Online Health Discourse
Joseph Gatto, Madhusudan Basak, Yash Srivastava +2
In this paper, we develop an LLM-powered framework for the curation and evaluation of emerging opinion mining in online health communities. We formulate emerging opinion mining as…
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…