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Why AI Chatbots Agree With You Even When You’re Wrong

View original at spectrum.ieee.org
IEEE Spectrum - Technical Title: Why AI Chatbots Agree With You Even When You’re Wrong Date: 2026-03-11 12:00 Source: https://spectrum.ieee.org/ai-sycophancy <img src="https://spectrum.ieee.org/media-library/conceptual-collage-of-emojis-being-poured-through-a-strainer-and-into-a-phone-judgmental-emojis-are-filtered-out…
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What we drew from this source

The claims Via News extracted from this document. We point to the source; we don't replace it.

  • The update we removed was overly flattering or agreeable—often described as sycophantic

    60% confidence
  • Model performance may degrade over long conversations because models get confused as they consolidate more text

    60% confidence
  • Sycophantic AI might lie to us and hide bad news in order to increase our short-term happiness

    60% confidence
  • When an AI receives a minor misgiving about its answer, it flips to agree with the user

    60% confidence
  • Reinforcement learning increased sycophancy, with one of the biggest predictors of positive ratings being whether a model agreed with a person's beliefs and biases

    60% confidence
  • If a user states a belief in a presupposition, the model will go along with it because that's what people normally do in conversations

    60% confidence
  • ChatGPT may correctly point to a suicide hotline when someone first mentions intent, but after many messages over a long period of time, it might eventually offer an answer that goes against our safeguards

    60% confidence
  • We just need to ask ourselves as a society, What do we want? Do we want a yes-man, or do we want something that helps us think critically?

    60% confidence
  • The thing that was most surprising is that these relatively simple fixes can actually do a lot to reduce sycophancy

    60% confidence
  • AI engaged my intellect, fed my ego, and altered my worldviews leading to psychiatric hospitalization

    60% confidence
  • Pretrained LLMs were already sycophantic before reinforcement learning

    60% confidence

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The same metric (eps) for the same entity (Morgan Stanley & Co. LLC) reported for the identical fiscal period (Q1 2026) and observation date (2026-03-31) has two conflicting values: 3.43 USD_per_share vs 3.08 USD. This is not a temporal change — both observations claim to measure the same point in time. The ~10% discrepancy (0.35 USD difference) is material for a financial metric.
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