1 citations · 1 across the 8 of their papers we have counts for
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Understanding Clinical Cognitive Dialogues Using Large Language Models
Vishalakshi Arumugam, Dan Schumacher, Veronica Rammouz +4
In-person cognitive assessment is both a test and an interaction. Clinicians explain tasks, repair misunderstandings, and adapt to patient responses, while patients may hesitate, s…
MedProb: Probing Internal Representations of Vision-Language Models for Medical Question Answering
Erfan Nourbakhsh, Ke Yang, Anthony Rios
Medical visual question answering (Med-VQA) is often assumed to require medical fine-tuning, large models, or complex multi-agent pipelines. We revisit this assumption with \textbf…
Detecting AI Impostors: How Do Middle Schoolers Identify LLM Agents in a Live Collaborative Setting?
Dan Schumacher, Pragathi Durga Rajarajan, Haven Kotara +6
LLMs can imitate how people write, which raises concerns about impersonation, trust, and detection in social settings. These concerns are especially important for adolescents, who…
TRACER: Early Failure Detection for Task-Oriented Dialogue
Erfan Nourbakhsh, Rocky Slavin, Ke Yang +1
Task-oriented dialogue systems often fail before the final breakdown is obvious, but most evaluation only measures failure after the conversation has already gone wrong. We present…
When Retrieval Doesn't Help: A Large-Scale Study of Biomedical RAG
Erfan Nourbakhsh, Rocky Slavin, Ke Yang +1
Medical question answering is a high-stakes setting where factual errors can have serious consequences. Retrieval-augmented generation (RAG) is widely viewed as a promising solutio…
Prompting Underestimates LLM Capability for Time Series Classification
Dan Schumacher, Erfan Nourbakhsh, Rocky Slavin +1
Prompt-based evaluations suggest that large language models (LLMs) perform poorly on time series classification, raising doubts about whether they encode meaningful temporal struct…
Telling Speculative Stories to Help Humans Imagine the Harms of Healthcare AI
Xingmeng Zhao, Tongnian Wang, Dan Schumacher +2
Artificial intelligence (AI) is rapidly transforming healthcare, enabling fast development of tools like stress monitors, wellness trackers, and mental health chatbots. However, ra…
Team UTSA-NLP at SemEval 2024 Task 5: Prompt Ensembling for Argument Reasoning in Civil Procedures with GPT4
Dan Schumacher, Anthony Rios
In this paper, we present our system for the SemEval Task 5, The Legal Argument Reasoning Task in Civil Procedure Challenge. Legal argument reasoning is an essential skill that all…