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20192026
most citedSingle Sample Feature Importance: An Interpretable Algorithm for Low-Level Feature Analysis

2 citations · 8 across the 18 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.AI2024★ 1 cited

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…

cs.CL2024

Explicit, Implicit, and Scattered: Revisiting Event Extraction to Capture Complex Arguments

Omar Sharif, Joseph Gatto, Madhusudan Basak +1

Prior works formulate the extraction of event-specific arguments as a span extraction problem, where event arguments are explicit -- i.e. assumed to be contiguous spans of text in…

cs.CL2024

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…

cs.CL2024

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…

cs.CL2024

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…

cs.CL2024

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…