31 citations · 36 across the 11 of their papers we have counts for
11 papers
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…
Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit
Nur Yildirim, Susanna Zlotnikov, Deniz Sayar +14
Advances in artificial intelligence (AI) have enabled unprecedented capabilities, yet innovation teams struggle when envisioning AI concepts. Data science teams think of innovation…
Align before Attend: Aligning Visual and Textual Features for Multimodal Hateful Content Detection
Eftekhar Hossain, Omar Sharif, Mohammed Moshiul Hoque +1
Multimodal hateful content detection is a challenging task that requires complex reasoning across visual and textual modalities. Therefore, creating a meaningful multimodal represe…
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…