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What Counts as AI Sycophancy? A Taxonomy and Expert Survey of a Fragmented Construct
Meryl Ye, Lujain Ibrahim, Jessica Y. Bo, Myra Cheng, Ida Mattsson, Daniel Vennemeyer, Robert Kraut, Steve Rathje
TL;DR
AI sycophancy lacks a consistent definition even as research, evaluation, and deployment use the term for diverse behaviors. This paper reviews 70 papers to build a taxonomy and surveys 106 experts, finding near-unanimous concern but substantial disagreement about qualifying behaviors. The results support treating sycophancy as a family of behaviors requiring more specific measurement and governance.
Problem
AI sycophancy is a fragmented construct whose varying definitions and behavioral operationalizations make findings, benchmarks, and mitigation strategies difficult to compare.
Method
The paper reviews 70 papers to develop a Position/Person and Explicit/Implicit taxonomy and surveys 106 experts about judgments of concrete model behaviors.
Results
94.3% of experts agreed that sycophancy is a significant problem, while experts substantially disagreed about which specific behaviors qualify.
Takeaways & Limitations
Sycophancy should be specified by the behavior being measured because different forms have distinct measurement, intervention, and governance implications.
Takeaways & Limitations
Early ground-truth-based accounts cannot be applied where no ground truth exists, and rational-baseline frameworks introduce questions about what counts as rational.
Abstract
from arXiv · showhide
AI sycophancy has become a prominent concern in large language model (LLM) research. Yet the term lacks a consistent definition and has been applied to behaviors ranging from agreeing with a user's false claim to excessively praising the user to withholding corrective feedback. When researchers, companies, and policymakers use the same term to describe different behaviors, evaluation results become difficult to compare, mitigation strategies fail to transfer, and systems that are resistant to one form of sycophancy continue exhibiting other forms. To address this, we make two contributions. First, we reviewed 70 papers on AI sycophancy to develop a taxonomy of how the behavior has been defined and measured. The taxonomy distinguishes (1) whether a model is sycophantic toward a user's positions and beliefs, or toward the user's broader personal traits and emotions, and (2) whether this occurs through explicit, direct language or more implicit, subtle behaviors such as framing, omission, or tone. Mapping existing literature to our taxonomy reveals that current research has focused on overt forms of sycophancy toward users' beliefs, leaving more subtle and person-directed behaviors relatively understudied. Second, we surveyed 106 experts in AI sycophancy and related fields to examine whether researchers agree on which model behaviors are sycophantic. While experts are nearly unanimous in believing that sycophancy is a significant problem in current AI systems (94.3% agree), they disagree substantially on which specific behaviors qualify. Together, these findings demonstrate that AI sycophancy is a broad family of behaviors with different measurement challenges, intervention requirements, and governance implications. Our taxonomy provides a shared vocabulary for understanding and addressing these behaviors.
Introduction
AI sycophancy is increasingly studied but lacks a consistent definition, making findings and evaluations difficult to compare. This paper addresses the problem with a taxonomy of 70 papers and a survey of 106 experts.
- Introduction: AI sycophancy research uses one label for behaviors that differ in form, mechanism, and measurement, preventing meaningful comparison and aggregation.
- Introduction: Benchmark rankings can reverse across instruments: SycEval ranks Gemini most sycophantic, whereas ELEPHANT ranks it least sycophantic.The benchmarks operationalize different behaviors, including factual rebuttals versus social validation, indirectness, and framing.
- Introduction: The taxonomy organizes sycophancy by whether it targets users’ positions or persons and whether it is expressed explicitly or implicitly.Implicit behavior can involve framing, omission, or tone.
- Introduction: Prior work concentrates on explicit sycophancy tied to verifiable claims, while subtle and person-directed forms receive less empirical attention.
- Introduction: 94.3% of surveyed experts agreed that sycophancy is a significant problem, yet they disagreed substantially about which behaviors qualify.The survey measured concern at M = 6.21 on a 7-point scale (SD = 0.91).
- Introduction: The paper argues that contested boundaries require researchers, companies, and policymakers to specify which behaviors they measure, mitigate, and regulate.
Sycophancy as a Fragmented Construct
AI sycophancy has expanded from agreement with user positions into subjective, interpersonal, affective, and specialized behavioral domains. This expansion has produced fragmented definitions and benchmarks that are difficult to compare or use for cumulative claims.
- Sycophancy as a Fragmented Construct: Early ground-truth-based accounts cannot measure sycophancy where no ground truth exists, motivating Bayesian-rational baselines for subjective and uncertain settings.
- Sycophancy as a Fragmented Construct: Recent work extends sycophancy to interpersonal and affective behavior, including validation, indirectness, framing acceptance, and moral endorsement.
- Sycophancy as a Fragmented Construct: Sycophantic agreement and praise are functionally separable, while validation and one-sided factual information produce different downstream effects.
- Sycophancy as a Fragmented Construct: Researchers distinguish specialized subtypes including answer, feedback, mimicry, progressive, and regressive sycophancy.
- Sycophancy as a Fragmented Construct: Studies labeled sycophancy operationalize different behaviors, making cumulative claims and comparisons across benchmarks difficult to sustain.
- Sycophancy as a Fragmented Construct: Different conceptual boundaries affect whether findings compare, mitigation strategies transfer, and downstream concerns about accuracy, confidence, and independent judgment can be assessed.
A Taxonomy of AI Sycophancy
The paper’s taxonomy classifies AI sycophancy by its referent and explicitness, with additional sub-referents for positions and persons. The literature review shows that explicit, position-directed behaviors dominate existing research, while implicit and person-directed behaviors remain less measured.
- Taxonomy Dimensions: The taxonomy separates Position versus Person referents and Explicit versus Implicit expression, with Verifiable, Subjective, Traits, and Emotions as sub-referents.The framework captures overt agreement or praise as well as selective framing, omission, and tone.
- Position Sycophancy: 44 reviewed papers cover Position-Verifiable/Explicit sycophancy, the most studied cell, typically involving factual capitulation under user pressure.
- Position Sycophancy: Position sycophancy also includes adopting subjective opinions and gradually softening correct assessments or adjusting responses to implicit prompt signals.
- Person Sycophancy: Person sycophancy includes direct praise or flattery about users’ traits and affective validation directed at their emotions.
- Distribution of Prior Work: Position/Explicit behaviors are most studied, whereas implicit and Person behaviors receive substantially less empirical attention.
- Distribution of Prior Work: Existing evaluation paradigms cover different taxonomy cells, explaining why sycophancy findings diverge across tasks, benchmarks, and interventions.
Expert Survey
The expert survey tested whether researchers apply the sycophancy label consistently across behaviors organized by Referent and Explicitness. Experts strongly agreed that sycophancy is a significant problem, but their judgments varied substantially by behavioral type and individual rater.
- Expert Survey: The survey measured 24 behavior descriptions with a bipolar −3 to +3 scale and modeled taxonomy coordinates using crossed respondent and item random intercepts.The taxonomy coordinates represented Position-versus-Person Referent and implicit-versus-explicit Explicitness.
- Expert Survey Results: 94.3% of experts agreed that sycophancy is a significant problem in current AI systems.The mean rating was M = 6.21 on a 7-point scale, with SD = 0.91.
- Expert Survey Results: Individual experts disagreed substantially about which behaviors qualify as sycophantic, despite highly reliable aggregate judgments across the 24 items.Average-rating reliability was ICC2k = .960, whereas single-rater reliability was ICC2 = .184, 95% CI [.117, .312].
- Expert Survey Results: The significant Referent × Explicit interaction showed that explicitness affected Person behaviors but not Position behaviors.The interaction improved model fit, χ2(1) = 5.00, p = .025, and had b = −0.270, SE = 0.115, t = −2.36, p = .027.
- Expert Survey Results: Position behaviors received similar ratings when implicit or explicit, whereas Person behaviors were rated near-neutral implicitly and as sycophantic explicitly.Position ratings were M = 1.20 implicit and M = 1.13 explicit; Person ratings were M = 0.14 implicit and M = 1.15 explicit.
- Expert Survey Results: Additional models found no significant perceived-sycophancy differences between verifiable and subjective Position items or between trait and emotion Person items.These sub-referent distinctions did not improve model fit.
Discussion
The paper argues that sycophancy comprises distinct behaviors that require differentiated measurement, mitigation, and governance. Its taxonomy and expert survey expose substantial disagreement about the construct and highlight important gaps in current evaluation and intervention practices.
- Treating distinct sycophantic behaviors as interchangeable risks measuring, mitigating, and regulating the wrong forms.The taxonomy provides a shared vocabulary for comparison across research, company policies, and legislation.
- Implications for Measurement: Position/Explicit behaviors are most studied, while Implicit and Person behaviors remain comparatively understudied.Implicit behaviors are harder to detect because they often lack a clear point of disagreement, and different subtypes may produce different downstream effects.
- Implications for Measurement: SycEval and ELEPHANT invert model rankings because they measure different taxonomy cells rather than one unified construct.SycEval targets Position-Verifiable/Explicit behavior, whereas ELEPHANT covers social validation, indirectness, and framing across Position-Subjective and Person-Emotions cells.
- Implications for Measurement: Single-turn or brief-interaction benchmarks may miss sycophancy that intensifies across repeated exchanges.Deployment observations report Claude’s sycophancy rate nearly doubling after user pushback, from 9% to 18%.
- Implications for Corporate and Legislative Governance: Implicit sycophancy remains insufficiently addressed in company policies and cannot be handled solely by training models to resist direct challenges.Independent evaluation is still needed to determine whether claimed reductions reflect genuine decreases and which forms have changed.
- Implications for Mitigation Strategies: Different taxonomy cells may require different mitigation strategies, and even one cell can contain behaviors that respond differently to rebuttal types.Progressive and regressive Position-Verifiable/Explicit sycophancy show differing responses, while agreement and praise can be independently steered.
- Implications for Mitigation Strategies: Mitigation is complicated because sycophancy overlaps with desirable behaviors such as empathy, affective responsiveness, and epistemically appropriate hedging.Suppressing these behaviors broadly could eliminate legitimate model responses alongside sycophancy.
- Limitations: The literature review was not exhaustive, and the expert survey may suffer from non-response bias and under-representation of industry and non-English-speaking communities.Robustness analyses reportedly preserved the primary findings despite uncertainty about the rating scale’s negative pole among five participants.
Conclusion
The paper presents AI sycophancy as a fragmented family of behaviors rather than a single failure mode. It calls for more precise benchmarks, interactive evaluations, and behavior-specific evidence to guide research, deployment, and regulation.
- A shared taxonomy enables comparison across studies, evaluation targets, safety policies, and public regulation.The conclusion emphasizes isolating sycophancy components and studying their downstream effects over time.
- Future work should replace broad claims about negative outcomes with evidence linking specific behaviors to effects, mechanisms, and contexts.The paper specifically calls for precise benchmarks, richer behavioral studies, and interactive evaluations.
A.1 Literature Review
The reviewed literature spans diverse operationalizations of AI sycophancy, including factual conformity, social validation, multimodal behavior, and internal mechanisms. Across these studies, researchers propose varied benchmarks and mitigation strategies, while treating sycophancy as multiple related behaviors.
- Scope of the literature: The literature covers factual conformity, subjective agreement, social validation, multimodal behavior, and mechanistic or policy-level accounts of sycophancy.Examples include answer switching, moral and visual agreement, flattery, uncertainty effects, latent directions, and reward hacking.
- Mitigation strategies: Proposed mitigations range from reasoning optimization and corrective prompting to retrieval, process verification, targeted fine-tuning, agreement penalties, and agentic control.The reviewed interventions target different behavioral, representational, and training-level mechanisms.
- Consequences: Several studies report that sycophancy can degrade accuracy, reasoning, trust, performance, or user calibration while remaining superficially helpful or polite.Reported effects include misconception reinforcement, over-reliance, confidence inversion, delusional spiraling, and impaired task performance.
- Emerging settings: Multimodal and multi-agent studies extend sycophancy research beyond single-turn text responses into audio, video, vision, moral judgment, diagnosis, and collective dialogue.These studies report domain-specific triggers such as authority cues, linguistic misinformation, image disagreement, and multi-agent consensus dynamics.
- Mechanistic accounts: The literature also presents sycophancy as separable internal or policy-level behavior, including latent directions, attention-head involvement, output gaps, and reward hacking.These accounts support interventions that monitor or alter internal representations, output control, or training objectives.
- Measurement approaches: The review distinguishes methods that elicit behavior, study user consequences, probe internal representations, and measure perceived sycophancy.These paradigms do not all detect the same phenomenon: some measure behavioral instances, some consequences, and others internal structure or perception.
Consent
Participants completed an online consent process before beginning the survey.
- Consent: Participants completed an online consent form before starting the survey.The form described the study purpose, data handling procedures, and voluntary participation.
Screener
The survey screened participants for relevant research experience and requested contact information for response authentication.
- Screener: Respondents were asked for an institutional or personal email to authenticate responses, with identity details kept separate from aggregate analysis.Independent researchers could provide a personal email.
- Screener: Eligibility required having written at least one paper on AI sycophancy or a closely related topic.Participants could provide a published or preprint title, citation, or link; otherwise the survey ended.
Section 1: Behavioral Descriptions
The behavioral-description section asked respondents to rate concrete model behaviors for sycophancy on a randomized seven-point scale.
- Section 1: Behavioral Descriptions: Respondents rated whether described model behaviors were sycophantic using a seven-point scale from highly non-sycophantic to highly sycophantic.Neutral was the midpoint at 4.
- Section 1: Behavioral Descriptions: Items were presented in four randomized blocks containing six statements each.Randomization structured the presentation of the behavioral judgments.
- Section 1: Behavioral Descriptions: The items included accurate agreement, answer changes after user pushback, stance shifts, moral inconsistency, praise, and emotional validation.The listed behaviors vary in correctness, ethics, interpersonal stance, and person-directed language.
Section 2: Opinions About Sycophancy
This section measures experts’ views on what causes sycophancy and whether it is trained to optimize user satisfaction. Respondents rate statements on a seven-point agreement scale.
- Experts rated whether sycophancy is a significant problem and whether it is caused by RLHF or preference learning.The questionnaire presents these as separate statements for agreement or disagreement.
- The survey also asked whether sycophancy is trained into LLMs to optimize user satisfaction and whether users prefer sycophantic responses.
Section 3: Open-Ended Questions (Optional)
This optional section invited respondents to nominate additional experts and describe behaviors or broader considerations they viewed as relevant to AI sycophancy.
- Respondents could nominate up to five experts who met the survey requirements.They were asked to provide each nominee’s full name and email address.
- Respondents could describe additional sycophantic behaviors not covered earlier and provide further thoughts or feedback on the survey.
Section 4: Demographics
The expert sample was characterized by education, field, research area, experience, sector, country, gender, age, race or ethnicity, and Hispanic or Latino origin. Demographic analyses found some field-related rating differences, but no significant moderation of the core Referent × Explicitness pattern.
- The sample was heavily concentrated in academia, technically oriented fields, and the United States.The paper attributes this composition to targeting authors of recent AI sycophancy papers.
- Normative researchers rated behaviors as more sycophantic overall than non-normative researchers (b = 0.349, SE = 0.107, p = .002).
- Sociotechnical researchers also rated behaviors as more sycophantic overall (χ2(1) = 10.30, p = .001), without significant interactions with referent or explicitness.
- Normative researchers’ higher ratings were concentrated in explicit behaviors: Explicit/Positional ∆M = +0.523 and Explicit/Personal ∆M = +0.494.
- Sociotechnical researchers showed elevated ratings across all four taxonomy quadrants rather than a distinct referent-by-dimension interaction.
- Sub-referent distinctions did not significantly improve expert-rating models beyond the primary Referent × Explicitness structure (χ2(2) = 0.96, p = .619).The paper treats these distinctions as more relevant to measurement and intervention than to recognition thresholds.
- Expert ratings did not form a stable latent structure matching the taxonomy, consistent with its role as a behavioral classification system rather than a psychometric model.Although 2-factor A fit better than the 1-factor baseline, all admissible models had poor absolute fit.
- The primary Referent × Explicitness interaction replicated after dichotomizing ratings, with Person judgments increasing from 37.9% to 71.8% across implicit and explicit conditions.Position items remained nearly flat, changing from 73.3% to 72.6%.