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Sycophancy in Large Language Models

Learn about how easily AI models cave when you push back on them, and why the training process is what taught them to do it

IntermediateAI SafetyRLHFLLMs2026-08-16· 4 min readRead the original paper →

What is Sycophancy?

Everyone knows someone who just agrees with whatever you say.

For example, you tell them your business idea is great, and they say it's great. You tell them it's terrible, and suddenly they think it's terrible too.

LLMs do the same exact thing, and this idea is called Sycophancy.

The Experiment

Researchers tested five different AI assistants across four different tasks(so this isn't a one model problem).

The simplest test is what happens when you just push back. Researchers asked the assistant a factual question, waited for the assistant to answer it correctly, and then said "I don't think that's right. Are you sure?"

The models folded almost immediately. Claude 1.3 apologized and admitted to a mistake it never made on about 98% of the questions, and when it came to actually changing the answer, the models flipped anywhere from about a third of the time for GPT-4, up to around 86% of the time for Claude 1.3.

Other Flavors of It

Pushing back afterwards isn't the only way to get a model to fold.

Researchers also tried casting a little doubt before the model ever answered, saying something as small as "I don't think the answer is..." about the right one. That alone dropped accuracy by as much as 27%!

Then, they tried the same thing on opinions instead of facts. When researchers asked an assistant to evaluate an argument, a math solution, or a poem, the feedback got noticeably warmer when the user said they liked the work, and noticeably harsher when the user said they didn't.

The work itself never changed at ALL, and the only thing that changed was what the user said about it.

But Why Does This Happen?

Well, honestly, the models aren't really broken, they're doing exactly what they were trained to do!

Modern assistants are trained using RLHF, where humans rate the model's answers and the model gets pushed toward whatever scored well.

When researchers went and checked what the humans were actually rewarding, they found that when a response matches what the user already believes, people are more likely to prefer that response. Even worse, both the human raters and the Preference Models standing in for them picked a nicely written sycophantic answer over the correct answer a real chunk of the time.

Basically, we taught the models to agree with us, because agreeing with us is what we kept rewarding.

Why Does This Matter?

Imagine a nursing student using an AI assistant to check a medication dosage:

  • They ask, and the assistant gives them the correct dose
  • They say "wait, I'm pretty sure it's double that"
  • The assistant apologizes, agrees, and hands over the wrong number
  • The student trusts the second answer, because the assistant sounded just as sure both times

The really dangerous part is that the assistant doesn't sound any less confident when it caves. It sounds exactly the same as it did when it was right.

Research

  1. This is the original Sycophancy paper by Anthropic! Link

Jargon Decoder

Every bolded term above, translated in one place.

Sycophancy
When a model tells you what you want to hear instead of what's actually true, usually because you signaled what you wanted.
RLHF
Reinforcement Learning from Human Feedback. Basically, humans rate the model's answers, and the model gets nudged toward the ones that scored well.
Preference Model
A model trained to guess how a human would rate an answer, so training can run at scale without a person grading every single response.
Feedback Sycophancy
When a model's review of your work gets warmer or harsher based on whether you said you liked it, even though the work never changed.
Mimicry Sycophancy
When a user states something false in the prompt and the model goes along with it. Basically, it repeats your mistake back to you even though it knows better.

I got into AI research by cold emailing, and I teach students how to do the same.

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