Workshops Archive

Generating Frequently Asked Questions from Technical Support Tickets using Large Language Models

Workshop: HUST-25: 12th International Workshop on HPC User Support Tools

Authors: Christina Joslin (Purdue University), David Burns (University of Wisconsin-Madison), and FNU Ashish and Elham Sarbijan (Purdue University)

Abstract: High-performance computing drives scientific discovery, but increasing system complexity and user demands generate a growing volume of diverse technical support issues. This trend underscores the need for automated tools that can extract clear, accurate, and relevant frequently asked questions from support tickets. We addressed this need by developing a novel pipeline for autonomous technical support that began by filtering tickets by anomaly frequency and recency. An instruction-tuned a large language model, then cleaned and summarized tickets. Next, unsupervised semantic clustering identified subclusters of similar tickets within broader topics, which were globally ranked by size, cohesion, and separation. A generation module powered by a large language model produced structured lists of frequently asked questions from the top-ranked subclusters. Evaluation by subject matter experts confirmed that our method produced understandable, accurate, and pertinent content. The extraction of detailed insights from ticket data enhances efficiency of support workflows and facilitates scientific research.

Website: https://1drv.ms/p/c/3fd70fe294678b56/ESBL5FwYvy5Bm4gOD3An72oB6M_wB9_lhQIKLNdT9cndLQ?e=pSFhVJ

Back to HUST-25: 12th International Workshop on HPC User Support Tools Archive Listing Back to Full Workshop Archive Listing