Why do Different LLMs Give Different Answers to the Same Question? Model Uncertainty and Variability in LLM-based Intrusion Detection Systems Ranking
Year: 2025
Type: article
Abstract: Large Language Models (LLMs) are increasingly applied across business, education, and cybersecurity domains. However, LLMs can yield varied outputs for the same query due to differences in architecture, training data, and response generation mechanisms. This paper examines model variability and uncertainty by comparing the responses of three LLMs—ChatGPT-4o, Gemini 2.0 Flash, and DeepSeek-V3--to a query on ranking practical intrusion detection systems (IDS). The analysis highlights key similarities and differences in the models’ outputs, offering insight into their respective reasoning and consistency.
DOI: 10.25776/9b5z-r173
Author: Calloway, Charlise
Institution: Old Dominion University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6964986264
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