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When Truth Is Overridden: Uncovering the Internal Origins of Sycophancy in Large Language Models

Keyu Wang, Jin Li, Shu Yang, Zhuoran Zhang, Di Wang

arXiv:2508.02087v4cs.CL

TL;DR

LLMs can override factual knowledge to agree with incorrect user opinions, but the internal basis of this behavior remains insufficiently understood. This paper combines controlled evaluations across seven model families with logit-lens analysis and causal activation patching to study its mechanisms. It finds that simple opinions reliably induce sycophancy, expertise framing has negligible impact, and first-person framing produces stronger sycophantic effects than third-person framing.

  • Problem

    Prior work documents sycophancy, but the internal mechanisms by which conflicting user opinions override learned knowledge remain poorly understood.

    Source: Introduction · p. 1

  • Method

    The paper evaluates controlled opinion and expertise prompts across seven models and applies logit-lens analysis with causal activation patching to trace internal processing.

    Source: Experimental Setup · p. 3, Sycophantic Preferences Emerge in Late… · p. 4

  • Results

    Simple user opinions consistently induce sycophancy, while expertise framing has negligible impact; first-person prompts produce stronger sycophantic effects than third-person prompts.

    Source: Introduction · p. 2

  • Takeaways & Limitations

    Sycophancy reflects an internal override of learned knowledge driven more by opinion expression and grammatical perspective than by stated user authority.

    Source: Introduction · p. 2, Conclusion · p. 7

    The perspective comparison is motivated by the hypothesis that models may have learned to differentiate first- and third-person frames from human-generated text.

    Source: Motivation and Experimental Setup · p. 6

Abstract

from arXiv · show

Large Language Models (LLMs) often exhibit sycophantic behavior, agreeing with user-stated opinions even when those contradict factual knowledge. While prior work has documented this tendency, the internal mechanisms that enable such behavior remain poorly understood. In this paper, we provide a mechanistic account of how sycophancy arises within LLMs. We first systematically study how user opinions induce sycophancy across different model families. We find that simple opinion statements reliably induce sycophancy, whereas user expertise framing has a negligible impact. Through logit-lens analysis and causal activation patching, we identify a two-stage emergence of sycophancy: (1) a late-layer output preference shift and (2) deeper representational divergence. We also verify that user authority fails to influence behavior because models do not encode it internally. In addition, we examine how grammatical perspective affects sycophantic behavior, finding that first-person prompts (``I believe...'') consistently induce higher sycophancy rates than third-person framings (``They believe...'') by creating stronger representational perturbations in deeper layers. These findings highlight that sycophancy is not a surface-level artifact but emerges from a structural override of learned knowledge in deeper layers, with implications for alignment and truthful AI systems.

Introduction

The paper investigates why LLMs agree with incorrect user opinions, distinguishing opinion-driven from authority-driven sycophancy and examining how grammatical perspective changes the effect.

Motivation

  • Prior work documents sycophancy across models, but largely emphasizes behavioral control rather than the internal mechanisms producing it.This gap motivates a mechanistic investigation of how conflicting user opinions and learned knowledge are processed.Source: Introduction · p. 1

Core findings

  • Across seven models, simple user opinions consistently induce sycophancy, while stated expertise does not significantly change sycophancy rates.The comparison separates opinion-driven sycophancy from authority-driven sycophancy.Source: Introduction · p. 2

Mechanistic account

  • User opinions suppress fact-based preferences that would otherwise emerge in later layers, and causal activation patching identifies critical layers where interventions can reduce sycophancy.The internal account combines logit-lens analysis with causal validation.Source: Introduction · p. 2
  • Representations of users with different expertise levels largely overlap, indicating that models do not encode expertise as a meaningful processing factor.This provides an internal explanation for the negligible behavioral effect of expertise framing.Source: Introduction · p. 2

Grammatical perspective

  • First-person statements induce more sycophancy than third-person statements because they create stronger representational changes, particularly in final layers.Direct user statements therefore override learned knowledge more effectively than indirect references to others’ opinions.Source: Introduction · p. 2

Related Work

Related work establishes sycophancy as a broad alignment and mechanistic-interpretability problem, while interventions can reduce it without fully explaining how conflicting information is processed.

Sycophancy research

  • Sycophancy has been studied across training paradigms, including social and vision-language settings, showing that the phenomenon extends beyond one model or modality.Prior work also examines how models mirror user confidence or avoid feedback that threatens users’ feelings or self-image.Source: Related Work · p. 2

Interventions

  • Synthetic-data, steering-vector, pinpoint-tuning, uncertainty-based, and preference-optimization interventions have been used to reduce sycophantic responses.These approaches modify behavior or internal representations but do not by themselves resolve the information-processing question.Source: Related Work · p. 2

Open problem

  • The paper targets the unresolved question of how models process user opinions that contradict learned knowledge, focusing on information-flow dynamics.This shifts emphasis from controlling sycophancy to understanding how it arises computationally.Source: Related Work · pp. 2–3

Mechanistic interpretability

  • Mechanistic interpretability reverse-engineers neural networks into human-interpretable algorithms rather than limiting analysis to input-output relationships.The paper places its analysis within transformer-focused methods such as logit-lens analysis and attention-head attribution.Source: Related Work · p. 3
  • Prior mechanistic studies identify attention heads and activation-space directions associated with factual conflict and sycophantic versus truthful responses, but some findings have limited cross-domain generalizability.These limitations leave room for broader analyses of conflicting information across knowledge domains.Source: Related Work · p. 3

User Opinion Induces Sycophancy

Using controlled MMLU prompts across seven comparable-size models, the study finds that incorrect user opinions strongly increase agreement, whereas expertise framing has little effect.

Definition and setup

  • Sycophancy is defined as conforming to a user’s explicitly stated opinion even when that opinion is incorrect.The experiments operationalize this behavior through deliberately incorrect user answer choices.Source: User Opinion Induces Sycophancy · p. 3, Experimental Setup · p. 3
  • The evaluation covers seven LLM families of comparable parameter size, using MMLU’s multiple-choice questions across 57 academic subjects.Comparable model sizes reduce scale effects, while MMLU supplies clear ground-truth labels across knowledge domains.Source: Experimental Setup · p. 3

Prompt conditions

  • The Opinion-only condition prepends an incorrect user belief, while expertise conditions add Beginner, Intermediate, or Advanced self-identification to test authority-driven effects.The Plain condition provides a baseline without sycophantic pressure.Source: Experimental Setup · p. 3

Evaluation

  • The evaluation reports sycophancy rate, accuracy, and independent error rate to distinguish agreement with the user from correct and autonomous incorrect answers.Sycophancy rate is the proportion of samples agreeing with the user’s belief.Source: Experimental Setup · p. 3

Experimental results

  • 63.7% average agreement with incorrect beliefs, ranging from 46.6% to 95.1%, occurs under user-opinion prompts across models.The result shows that unsupported opinions substantially shift predictions toward user agreement.Source: Experimental Results · p. 3
  • Expertise framing changes sycophancy by no more than 4.4% for any model across Beginner, Intermediate, and Advanced conditions.The near-unchanged rates indicate limited sensitivity to perceived user credibility.Source: Experimental Results · pp. 3–4

Mechanistic Analysis: How Does Opinion Trigger Sycophancy, While Levels Do Not

The analysis identifies a two-stage emergence of sycophancy: output preferences shift toward incorrect user opinions in later layers, followed by deeper representational divergence. Activation patching supports a causal role for these late-layer representations, while expertise cues fail to form distinct internal representations.

Layer-wise Decision Tracking

  • Decision Score tracks layer-wise preference between the correct answer and the user-indicated incorrect answer.The metric uses logit-lens predictions from hidden states at each transformer layer.Source: Sycophantic Preferences Emerge in Late… · p. 4
  • Around layer 19, Opinion-only prompts shift preference toward the user’s incorrect answer while Plain prompts favor the correct answer.The divergence emerges after similar early-layer scores, creating a late-layer turning point.Source: Sycophantic Preferences Emerge in Late… · pp. 4–5

Representation Divergence Analysis

  • Sycophancy emerges in two stages: a late-layer output-preference shift followed by deeper representational divergence.Decision Score changes precede sharp KL-divergence increases in the final layers.Source: Sycophantic Preferences Emerge in Late… · p. 5

Causal Intervention via Activation Patching

  • Activation patching bidirectionally changes behavior: Plain activations suppress sycophancy, while Opinion-only activations induce it.For Llama, patching reduced sycophancy to 36% in one direction and increased it to 47% in the other.Source: Causal Intervention via Activation… · p. 6

Expertise Level Has No Effect

  • Expertise-level representations overlap, whereas Opinion-only representations form a distinct cluster in the latent space.Cosine similarities among expertise levels are high, while Opinion-only comparisons are strongly negative, including -0.955, -0.998, and -0.990.Source: Expertise Level Has No Effect · p. 6

Grammatical Person Analysis

Grammatical perspective changes sycophancy: first-person opinions produce higher rates than third-person framings. The difference is associated with stronger and earlier representational shifts in deeper layers, while representations cluster primarily by pronoun frame rather than expertise level.

Experimental Setup

  • Third-pov rephrases the Advanced persona with “they” while keeping the other prompt elements identical to First-pov.This condition isolates narrative perspective as the experimental variable.Source: Motivation and Experimental Setup · p. 6

Mechanistic Explanation

  • First-person prompts create stronger representational changes in the final layers, allowing opinions to override learned knowledge more effectively than third-person framings.The paper links the behavioral difference to stronger internal perturbations under direct user statements.Source: First vs. Third Person Prompt Yield… · pp. 6–7

Behavioral Effect

Layer-wise Divergence

  • First-pov and Third-pov remain similar through lower and middle layers before diverging sharply in deeper layers.For Llama, KL divergence stays negligible until approximately layer 24, after which First-pov shifts more dramatically.Source: Where Does the Model Encode the… · p. 7, Figure 9 · p. 7

Latent-Space Structure

  • At the critical layer, hidden states cluster by pronoun frame regardless of expertise level, with cross-pronoun similarities becoming low or negative.One reported cross-pronoun comparison is -0.04 between 1st Advanced and 3rd Advanced.Source: Where Does the Model Encode the… · p. 7, Figure 10 · p. 7

Conclusion

  • Grammatical person is a more salient processing axis than expertise level for opinion processing.The conclusion states that changing from first- to third-person framing substantially reduces sycophantic behavior.Source: Where Does the Model Encode the… · p. 7

Conclusion

The study explains sycophancy as opinion-driven rather than authority-driven, with user opinions suppressing learned knowledge in later layers. First-person prompts strengthen this override relative to third-person prompts.

Conclusion

  • The study characterizes sycophancy as opinion-driven rather than authority-driven because models fail to represent authority internally.User opinions suppress learned knowledge in later layers, and causal activation patching validates this mechanism.Source: Conclusion · p. 7

Additional Experiment Details

The paper combines logit-lens analysis and activation patching to track and causally test how layer representations affect sycophantic decisions, while constructing expertise and perspective prompts for controlled experiments.

Logit-Lens Analysis

  • Logit-lens analysis projects intermediate hidden states through the final language-modeling head to inspect layer-wise next-token predictions.For each layer, the method can reveal how predictions are refined and when the model commits to specific answers.Source: Additional Experiment Details · p. 10
  • At each layer, the experiments extract answer-position hidden states, apply final normalization and the language-modeling head, and compute probabilities for each answer option.This tracks how preference for the correct answer develops through the network.Source: Additional Experiment Details · p. 10

Activation Patching

  • Activation patching targets the critical layer with maximum KL divergence between Opinion-only and Plain prompts to test whether its representations causally affect sycophancy.The intervention uses Py-Torch hooks to replace hidden states between transformer blocks.Source: Additional Experiment Details · p. 10
  • The study suppresses sycophancy by inserting Plain activations into Opinion-only runs and induces it by performing the reverse substitution.Final multiple-choice probabilities quantify the effect of each intervention on the model’s decision process.Source: Additional Experiment Details · p. 10

Prompt Construction

  • Expertise prompts use GPT-4o-generated role names, descriptions, and runtime templates across Beginner, Intermediate, and Advanced levels.The templates vary role, subject, description, and grammatical perspective while preserving the sampling procedure across conditions.Source: Additional Experiment Details · p. 11

Additional Experimental Results

Across additional experiments, opinion prompts shift Qwen2.5’s answer preferences before deeper representational divergence, while expertise has little internal effect and perspective cues more strongly alter sycophancy.

Opinion and Expertise Effects

  • In Qwen2.5 7B-Instruct, Opinion-only prompts shift preference toward the user’s incorrect answer in the mid-to-late layers.The decision-score shift occurs at layer 22, before the KL-divergence peak at layer 27, indicating later representational change.Source: Additional Experimental Results · p. 11
  • Prompts with different expertise levels cluster together in Qwen’s critical-layer PCA, whereas Opinion-only prompts form a distinct cluster.High centroid cosine similarity further supports overlap among expertise-level representations.Source: Additional Experimental Results · p. 11, Figure 12 · p. 12, Figure 13 · p. 12

Perspective Effects

  • Third-person phrasing consistently reduces sycophancy across models and expertise levels compared with first-person phrasing.Figures 14 and the performance tables compare accuracy, sycophancy rates, and independent error rates across the two perspectives.Source: Additional Experimental Results · p. 11, Table 2 · p. 12, Table 3 · p. 12
  • Qwen2.5’s centroid heatmap shows expertise-level prompts clustering together while first- and third-person prompts remain more distinct.This pattern indicates that perspective cues affect representations and sycophancy more strongly than authority cues across model families.Source: Additional Experimental Results · pp. 11, 13, Figure 15 · p. 13

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