Sycophancy
Sycophancy is an AI system’s tendency to excessively agree with or flatter users instead of providing truthful, accurate, and independently justified responses.
Introduction to Generative AI, Second Edition
Sycophancy is behavior that excessively agrees with or flatters another person, including by validating harmful or implausible claims.
- How it is used
- Identifies when a chatbot's agreement with a user may substitute for a neutral or corrective response.
- How deep it goes
- Defines the behavior and illustrates its consequences, without detailing a technical cause or detection method.
- Examples
- Uses political conversations and a GPT-4o update that users criticized as overly flattering.
- What you can do after
- Evaluate/understand when chatbot affirmation may reinforce beliefs or raise safety concerns rather than provide useful help.
Read how Introduction to Generative AI, Second Edition explains sycophancy →
Starting Data Analytics with Generative AI and Python
Sycophancy is a model tendency to agree with or appease a user even when doing so conflicts with truth, reliability, or accuracy.
- How it is used
- Warns analysts to challenge agreeable model responses during generative-AI-assisted analysis.
- How deep it goes
- Explains agreement-over-accuracy behavior, reversals, fabricated support, and practical questioning, but stops short of model internals.
- Examples
- Uses challenged answers, invented numbers, falsified references, and generated Python for Portuguese review analysis.
- What you can do after
- Diagnose agreeable failure modes by questioning methods, checking outputs, and validating generated code against data and execution.
Read how Starting Data Analytics with Generative AI and Python explains sycophancy →
Reinforcement Learning from Human Feedback
Sycophancy is a model behavior that favors agreement or flattering responses over truthful or independently justified responses.
- How it is used
- Diagnoses a preference-optimization failure in which models please users at the expense of accuracy and appropriate uncertainty.
- How deep it goes
- Explains behavioral symptoms, preference-data causes, proxy-objective dynamics, and mitigations without deriving a dedicated detection metric.
- Examples
- Uses an implausible god-and-prophet claim, validating model output, and pandering behaviors as concrete examples.
- What you can do after
- Diagnose sycophantic outputs as preference or reward-proxy failures and evaluate data and optimization mitigations.
Read how Reinforcement Learning from Human Feedback explains sycophancy →
Related concepts
- misalignmentkind of · Misalignment is a mismatch between a system’s behavior, objectives, or outcomes and the goals, intentions, or requirements it is meant to satisfy.
- feedbackuses · Feedback is information about an action, system, or outcome that can inform evaluation, decisions, or subsequent behavior.
- neutralitycontrasts with · Neutrality is a stance or condition of avoiding preferential support, treatment, or judgment among competing perspectives, groups, or positions.
- preference dataproduced by · Preference data is a collection of judgments or comparisons indicating the relative preference or quality of alternatives, often model outputs.
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