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Instruction-tuned Large Language Models (LLMs) can perform a wide range of tasks given natural language instructions to do so, but they are sensitive to how such instructions are phrased. This issue is especially concerning in healthcare, as clinicians are unlikely to be experienced prompt engineers and the potential consequences of inaccurate outputs are heightened in this domain. This raises a practical question: How robust are instruction-tuned LLMs to natural variations in the instructions provided for clinical NLP tasks? We collect prompts from medical doctors across a range of tasks and quantify the sensitivity of seven LLMs—some general, others specialized—to natural (i.e., non-adversarial) instruction phrasings. We find that performance varies substantially across all models, and that—perhaps surprisingly—domain-specific models explicitly trained on clinical data are especially brittle, compared to their general domain counterparts. Further, arbitrary phrasing differences can affect fairness, e.g., valid but distinct instructions for mortality prediction yield a range both in overall performance, and in terms of differences between demographic groups.
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This content will become publicly available on April 10, 2026
Why do Different LLMs Give Different Answers to the Same Question? Model Uncertainty and Variability in LLM-based Intrusion Detection Systems Ranking
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.
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- PAR ID:
- 10639432
- Publisher / Repository:
- Old Dominion University ODU Digital Commons
- Date Published:
- Subject(s) / Keyword(s):
- Intrusion Detection, Machine Learning
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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