LLM Support for Real-Time Technical Assistance
Abstract
In this paper, we present a demo web application that adopts Large Language Models (LLMs) to enhance user support across various fields. Its primary goal is to enable experts, like technicians, to deliver remote assistance more effectively by leveraging LLM capabilities. The application permits experts to browse through a database of documents, including past support chats and manuals, and suggests responses based on previous interactions. We developed the demo using publicly available data sets from technical support and tutoring domains to showcase its adaptability. Key features include search functionality, response suggestions, and automatic information extraction. The demo highlights the potential of LLMs in improving technical support workflows by streamlining knowledge retrieval and aiding technicians in resolving queries, leading to enhanced efficiency and user satisfaction in support interactions.
Cite
Text
Scotti and Carman. "LLM Support for Real-Time Technical Assistance." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2024. doi:10.1007/978-3-031-70371-3_26Markdown
[Scotti and Carman. "LLM Support for Real-Time Technical Assistance." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2024.](https://mlanthology.org/ecmlpkdd/2024/scotti2024ecmlpkdd-llm/) doi:10.1007/978-3-031-70371-3_26BibTeX
@inproceedings{scotti2024ecmlpkdd-llm,
title = {{LLM Support for Real-Time Technical Assistance}},
author = {Scotti, Vincenzo and Carman, Mark James},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2024},
pages = {388-393},
doi = {10.1007/978-3-031-70371-3_26},
url = {https://mlanthology.org/ecmlpkdd/2024/scotti2024ecmlpkdd-llm/}
}