✨LLMs
Researchers investigated how sycophancy—the tendency of LLMs to agree with users—affects the accuracy and safety of medical responses.
🔬 Methods
Models tested: GPT-4-Turbo, Claude-3, Gemini 1.5, and Llama-3-70B.
Evaluation: 1,200 medically validated questions covering evidence-based reasoning, bias, and misinformation traps.
Tasks: Compare truthful vs. user-biased prompts.
Assessment: Independent clinicians rated factual accuracy, citation reliability, and degree of agreement.
📊 Results
All models agreed with false statements 15–38 % of the time.
Claude-3 and GPT-4-Turbo maintained the highest accuracy (86–89 %) but still produced confident false information under user bias.
Gemini 1.5 and Llama-3-70B were more likely to mirror user opinion, especially on politically or ethically sensitive topics.
Safety filters reduced misinformation.
🔑 Key Takeaways
Sycophancy remains a safety risk in LLMs.
The way the user interact shapes the model accuracy—even with safety filters.
Future systems require bias-aware prompting and adversarial testing to ensure reliability in clinical use.
🔗 When Helpfulness Backfires: The Hidden Risks of Sycophancy in Medical AI. npj Digital Medicine 2025; DOI: 10.1038/s41746-025-02008-z.
🦾TechTool
Today, we will continue with our prompt series.
One-Shot / Few-Shot Prompting
Is showing the model one or a few shot examples before asking your real question.
This helps the model learn your format, tone, or reasoning style before generating an answer.
When is it useful?
It’s ideal when you want consistency or structure — like generating summaries, writing structures notes, or extracting key data.
Example:
“Here’s how I want you to summarize this: [insert short example].
Now summarize this document in the same format.”
By giving the model an example or two, you set a clear template.
The result is usually more accurate, consistent, and aligned with your intent — especially for specific tasks.
We’ll continue with Chain-of-Thought Prompting next week.
That’s all for today.
You’re already ahead of the curve in medical LLMs — don’t keep it to yourself. Forward AIMedily to a colleague who’d appreciate the insights.
Thank you!
Until next Wednesday.
Itzel Fer, MD PM&R
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