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cs.CL2026

Measuring Distribution Shift in User Prompts and Its Effects on LLM Performance

Parker Seegmiller, Sarah Masud Preum

LLMs are increasingly deployed in dynamic, real-world settings, where the distribution of user prompts can shift substantially over time as new tasks, prompts, and users are introd…

cs.CL2026

Medical Triage as Pairwise Ranking: A Benchmark for Urgency in Patient Portal Messages

Joseph Gatto, Parker Seegmiller, Timothy Burdick +4

Medical triage is the task of allocating medical resources and prioritizing patients based on medical need. This paper introduces the first large-scale public dataset for studying…

cs.CL2026

How Much Would a Clinician Edit This Draft? Evaluating LLM Alignment for Patient Message Response Drafting

Parker Seegmiller, Joseph Gatto, Sarah E. Greer +4

Large language models (LLMs) show promise in drafting responses to patient portal messages, yet their integration into clinical workflows raises various concerns, including whether…

cs.CL2025

Can LLMs Solve My Grandma's Riddle? Evaluating Multilingual Large Language Models on Reasoning Traditional Bangla Tricky Riddles

Nurul Labib Sayeedi, Md. Faiyaz Abdullah Sayeedi, Khushnur Binte Jahangir +2

Large Language Models (LLMs) show impressive performance on many NLP benchmarks, yet their ability to reason in figurative, culturally grounded, and low-resource settings remains u…

cs.CL2025

REGen: A Reliable Evaluation Framework for Generative Event Argument Extraction

Omar Sharif, Joseph Gatto, Madhusudan Basak +1

Event argument extraction identifies arguments for predefined event roles in text. Existing work evaluates this task with exact match (EM), where predicted arguments must align exa…

cs.CL2025

Follow-up Question Generation For Enhanced Patient-Provider Conversations

Joseph Gatto, Parker Seegmiller, Timothy Burdick +3

Follow-up question generation is an essential feature of dialogue systems as it can reduce conversational ambiguity and enhance modeling complex interactions. Conversational contex…