Publications
Showing 89 results for Author: Hilda B. Klasky
Mar, 2026
Conference Paper
Automating and Evaluating Large Language Models for Accurate Text Summarization Under Zero-Shot Conditions
Automated text summarization (ATS) is crucial for collecting specialized, domain-specific information. Zero-shot learning (ZSL) allows large language models (LLMs) to respond to prompts on information not included in their training, playing a vital role in this process. This study evaluates LLMs' effectiveness in generating accurate summaries under ZSL conditions and explo…
May, 2025
ORNL Report
VA EDH Advanced Software Pipeline Framework Report: Enhancing Automation and Scalability
May, 2025
Journal
Anomaly Detection in Electronic Health Records Across Hospital Networks: Integrating Machine Learning With Graph Algorithms
In a large hospital system, a network of hospitals relies on electronic health records (EHRs) to make informed decisions regarding their patients in various clinical domains. Consequently, the dependability of the health information technology (HIT) systems responsible for collecting EHR data is of utmost importance for patient safety. Recently, novel methods and tools aim…
Apr, 2025
ORNL Report
Automating the Analysis of Large Language Models Responses through Zero-Shot Question Answering
Recent advancements in Large Language Models (LLMs) have shown significant potential in various applications, yet their evaluation, particularly in zero-shot question answering scenarios, remains a challenging task. In this study, our objective was to explore precision metrics for Large Language Models (LLM) and design and implement a software pipeline to automatically eva…
Apr, 2025
ORNL Report
Prompt Phrase Ordering Using Large Language Models in HPC: Evaluating Prompt Sensitivity
Large language models (LLMs) have demonstrated effective performance in domain-specific tasks, often requiring a well-designed prompt to guide their responses. However, optimizing the right prompt is challenging due to prompt sensitivity—the phenomenon where small changes in the prompt can lead to significant variations in performance. In this study, we evaluate prompt per…