Lightweight LLMs lose 7.2 percentage points of accuracy when false health claims are injected into prompts, but only 1.4 points when medical jargon is replaced with everyday language.
Making texts in electronic health records comprehen- sible to consumers: A prototype translator,
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Evaluating LLM Robustness Under Domain-Specific Prompt Perturbations in Public Health Applications
Lightweight LLMs lose 7.2 percentage points of accuracy when false health claims are injected into prompts, but only 1.4 points when medical jargon is replaced with everyday language.