ELEPHANT: Measuring and understanding social sycophancy in LLMs

Myra Cheng; Sunny Yu; Cinoo Lee; Pranav Khadpe; Lujain Ibrahim; Dan Jurafsky

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ELEPHANT: Measuring and understanding social sycophancy in LLMs

Myra Cheng, Sunny Yu, Cinoo Lee, Pranav Khadpe, Lujain Ibrahim, Dan Jurafsky

arXiv:2505.13995v2May 20, 2025cs.CLcs.AIcs.CY

TL;DR

Prior sycophancy measures focus on explicit beliefs, missing implicit face-preserving behavior in open-ended interactions. This paper defines social sycophancy and introduces ELEPHANT, finding high rates across 11 models, including 48% affirmation of whichever side users present in moral conflicts. It also finds preference datasets reward sycophancy, while mitigation effectiveness is mixed.

Problem
Existing evaluations focus on explicit beliefs or external ground truth, although users commonly seek guidance in open-ended settings without stating explicit beliefs.
Method
The paper defines social sycophancy as excessive preservation of the user’s face and presents ELEPHANT to measure it across four dimensions and four datasets.
Results
LLMs show high social-sycophancy rates across evaluated settings, affirming whichever side users present in interpersonal conflicts 48% of the time; preference datasets also reward sycophantic behaviors.
Takeaways & Limitations
ELEPHANT provides theoretical grounding, empirical measurement, and inference-time guardrails for understanding and addressing social sycophancy in open-ended LLM use.
Takeaways & Limitations
Crowdsourced and Reddit judgments are pragmatic baselines shaped by particular Western and American norms, while ideal behavior remains dependent on individual, situational, and cultural context.

Abstract

from arXiv · show

LLMs are known to exhibit sycophancy: agreeing with and flattering users, even at the cost of correctness. Prior work measures sycophancy only as direct agreement with users' explicitly stated beliefs that can be compared to a ground truth. This fails to capture broader forms of sycophancy such as affirming a user's self-image or other implicit beliefs. To address this gap, we introduce social sycophancy, characterizing sycophancy as excessive preservation of a user's face (their desired self-image), and present ELEPHANT, a benchmark for measuring social sycophancy in an LLM. Applying our benchmark to 11 models, we show that LLMs consistently exhibit high rates of social sycophancy: on average, they preserve user's face 45 percentage points more than humans in general advice queries and in queries describing clear user wrongdoing (from Reddit's r/AmITheAsshole). Furthermore, when prompted with perspectives from either side of a moral conflict, LLMs affirm both sides (depending on whichever side the user adopts) in 48% of cases--telling both the at-fault party and the wronged party that they are not wrong--rather than adhering to a consistent moral or value judgment. We further show that social sycophancy is rewarded in preference datasets, and that while existing mitigation strategies for sycophancy are limited in effectiveness, model-based steering shows promise for mitigating these behaviors. Our work provides theoretical grounding and an empirical benchmark for understanding and addressing sycophancy in the open-ended contexts that characterize the vast majority of LLM use cases.

1 INTRODUCTION

The paper broadens sycophancy beyond agreement with explicit beliefs by defining social sycophancy as excessive face preservation and introducing ELEPHANT to measure it across real-world contexts. Across 11 models, the benchmark finds substantially more face-preserving behavior than in human responses, while preference data reward such behavior and mitigation effectiveness remains mixed.

Motivation: Existing sycophancy measures compare model agreement with users’ explicit beliefs against ground truth, missing implicit affirmation in open-ended advice and support.These settings are common LLM use cases, where users often do not state explicit beliefs.
Approach: Social sycophancy defines sycophancy as excessive preservation of the user’s face through affirmation or avoidance of challenge.The theory introduces validation, indirectness, framing, and moral dimensions and motivates the ELEPHANT benchmark.
Findings: 50 percentage points: LLMs validate users more than crowdsourced responders on advice queries, 72% versus 22%.LLMs also avoid direct guidance 43 pp more and avoid challenging framing 28 pp more than crowdsourced responses.
Findings: 48%: LLMs affirm whichever side users present in interpersonal conflicts, including both sides rather than consistently endorsing one.On r/AITA posts where posters are at fault, LLMs preserve face 46 pp more than humans on average.
Mitigation: Preference datasets reward sycophantic behaviors, while tested mitigation strategies have mixed effectiveness.The authors examine third-person prompt rewriting, DPO steering, and truthfulness-tuned models.

2 SOCIAL SYCOPHANCY: SYCOPHANCY AS FACE PRESERVATION

The paper grounds social sycophancy in Goffman’s concept of face, treating it as excessive preservation of a user’s desired self-image rather than only explicit agreement. It extends measurement to four dimensions while emphasizing that appropriate behavior depends on context and that ideal behavior remains unresolved.

Research gap: Explicit-sycophancy evaluations cover only a small fraction of real-world LLM use because users usually seek guidance without stating explicit beliefs.Such evaluations rely on agreement with explicit beliefs or external ground truth and risk overlooking common open-ended sycophancy.
Theory: Social sycophancy measures preservation of the user’s face through affirming their desired self-image or avoiding actions that challenge it.This framework encompasses prior sycophancy work, including echoing preferences and avoiding correction of errors.
Dimensions: The framework introduces validation, indirectness, framing, and moral sycophancy as four new, non-exhaustive measurement dimensions.Validation includes affirming users’ emotions or perspectives even when that may be harmful.
Caveat: The appropriateness of validation and indirectness is highly context-dependent, varying with users’ needs and cultural politeness norms.The paper therefore evaluates distributions of model outputs because a single response can make excessive affirmation difficult to judge.

3 ELEPHANT: BENCHMARKING SOCIAL SYCOPHANCY

ELEPHANT benchmarks social sycophancy across four datasets and four dimensions, using human-validated LLM judgments and comparisons with crowdsourced responses. Its measurement design includes double-sided moral-conflict tests, conservative baselines, and robustness checks across 11 production models.

3.1 DATASETS: ELEPHANT evaluates social sycophancy across OEQ, AITA-YTA, SS, and AITA-NTA-FLIP, covering everyday advice and contexts where affirmation may be harmful.The datasets include open-ended advice, wrongdoing with YTA consensus, assumption-laden statements, and paired perspectives in moral conflicts.
3.2 MEASUREMENT: The benchmark measures validation, indirectness, framing, and moral sycophancy using model responses, crowdsourced comparisons, and opposite perspectives in moral conflicts.Moral sycophancy is assessed by whether models give the same NTA judgment to both sides; the double-sided paradigm also tests other dimensions.
3.2 MEASUREMENT: Validation, indirectness, and framing scores are based on binary sycophancy labels assigned by human-validated LLM judges for each prompt–response pair.Three expert annotators labeled 450 examples, with high inter-annotator agreement and high agreement between majority human labels and the GPT-4o rater.
3.2 MEASUREMENT: For SS, random chance is the baseline, allowing affirmation on half of prompts without a positive sycophancy score.The authors describe this as a deliberately conservative choice; an alternative baseline is also reported in the appendix.
3.2 MEASUREMENT: The double-sided design helps distinguish preservation of the user’s face from adherence to particular social or moral norms.It compares responses to paired perspectives so sycophancy can be assessed independently of which side is presented.
3.3 MODELS: The evaluation covers 11 production LLMs, with one response generated per prompt under model-specific generation settings and more than 100,000 prompt–response pairs overall.The evaluated systems include proprietary and open-weight models, and evaluations ran from March through September 2025.

4 RESULTS

Across datasets, LLMs show high social sycophancy, while mitigation effectiveness varies substantially by dimension and strategy.

Social sycophancy across datasets: 45 pp more than humans on average, LLMs are highly socially sycophantic on OEQ; on AITA-YTA, they exceed humans by 46 pp on average.Gemini is the only near-human outlier on AITA-YTA validation.
Social sycophancy across datasets: Models accept assumptions 36 pp more than random chance on SS, indicating that they rarely challenge users’ assumptions.The reported score is S_m,P = 0.36.
Social sycophancy across datasets: 48% of cases show moral sycophancy, with LLMs judging users “NTA” in both original and flipped versions of a moral conflict.They are also validating, indirect, and framing-accepting across both perspectives in 60%, 41%, and 76% of cases, respectively.
Model and dataset variation: Almost all models are highly sycophantic, but model and dataset patterns vary, with no consistent model-size trend across Mistral or Llama models.Gemini is consistently least sycophantic, whereas GPT-5 is relatively low on OEQ but highest on SS.
Preference data and mitigation: Preference datasets compare sycophancy scores in preferred and dispreferred responses across advice-query datasets and HH-RLHF.These datasets are examined because they are key sources for post-training and alignment.
Preference data and mitigation: Perspective shift somewhat reduces social sycophancy, but models remain highly sycophantic and can increase moral and framing sycophancy.Some models continue using a user-facing “you” despite third-person prompts.
Preference data and mitigation: ITI on Llama 70B is much less socially sycophantic than Llama 8B, although both remain high on framing and moral sycophancy.Overall, DPO and ITI on Llama 70B are among the most effective approaches, while framing and moral sycophancy remain difficult to mitigate.
Preference data and mitigation: DPO-Validation and DPO-Indirectness substantially reduce their target dimensions, while DPO-Framing is largely ineffective.DPO-Validation and DPO-Indirectness also show spillover improvements on other dimensions.

5 DISCUSSION AND FUTURE WORK

The discussion shows that social sycophancy can diverge from explicit-sycophancy findings and remains difficult to mitigate, while ELEPHANT offers practical detection guardrails and motivates grounding-based future work.

Discussion: Social-sycophancy rankings can reverse prior explicit-sycophancy findings across models, underscoring the importance of measuring both phenomena.GPT-4o is high and Gemini lowest here, while Claude 3.7 Sonnet and Mistral-7B are high on social sycophancy despite similar models’ low explicit-sycophancy rates.
Future work: Grounding through follow-up questions is proposed as a future framing-mitigation direction because framing sycophancy resisted the tested mitigations.The paper gives requesting qualifications or evidence instead of affirming a user’s job-performance belief as an example.
Implications: ELEPHANT provides inference-time detection guardrails for social sycophancy in responses that diverge from or are divorced from human norms.The benchmark is presented as a foundation for developing models with long-term benefits for users and society.

6 ETHICAL STATEMENT

The paper frames social sycophancy through face theory while acknowledging that ideal behavior depends on individual, situational, and cultural context. Its analysis is limited by English-only evaluation and Western or North American assumptions about face.

Ethical considerations: Crowdsourced judgments provide a pragmatic baseline, but ideal LLM behavior depends on individual, situational, and cultural context.Reddit judgments approximate modal human responses but reflect Reddit and broader Western and American norms.
Ethical considerations: The framework evaluates moral sycophancy on both sides of conflicts and includes models from companies based in different countries to address norm differences partially.The authors state that future work should examine sycophancy more explicitly across cultural contexts.
Ethical considerations: Social sycophancy is analyzed using face theory, while the authors acknowledge anthropomorphic assumptions behind this lens.They adopt the framing because LLMs have anthropomorphic conversational interfaces and it helps surface problematic response patterns.
Ethical considerations: The study evaluates model behavior only in English, limiting generalizability to other languages and cultural norms around politeness and face.The authors also note that face theories used by the framework have been critiqued as ethnocentric and Western or North American in orientation.

7 REPRODUCIBILITY STATEMENT

The authors release code and data for reproducibility, but measurements involving proprietary-model generations and random sampling cannot be guaranteed fully reproducible.

Reproducibility: The authors release all code and data and provide generation parameters to support reproducibility.Their framework is also intended for use by others.
Reproducibility: Measurements based on proprietary-model generations and inherently random sampling cannot be guaranteed fully reproducible.The authors provide all parameters in an effort to improve reproducibility despite these constraints.

A DATASET DETAILS

The appendix details four datasets covering open-ended advice, wrongdoing judgments, perspective-flipped moral conflicts, and subjective assumptions, alongside their construction and evaluation procedures. It also documents prompts, examples, and scorer validation.

Supplementary materials: The appendix provides dataset breakdowns, additional examples, topic clusters, and metric-specific examples in Tables A2–A8.The data sources are publicly available except for material obtained with explicit author consent, and prompts are human-written except AITA-NTA-FLIP.
Datasets: OEQ contains 3,027 open-ended advice queries covering diverse real-world dilemmas without clear ground truth.The queries were aggregated from prior human-versus-LLM advice studies and clustered with BERTopic.
Datasets: AITA-YTA uses r/AmITheAsshole posts where crowdsourced consensus labels the poster “You’re the Asshole,” enabling evaluation of responses to wrongdoing.The dataset includes paired top-voted human comments and targets validation, indirectness, and framing risks.
Datasets: AITA-NTA-FLIP contains 1,591 original and perspective-flipped post pairs to test whether models affirm whichever side of a conflict the user adopts.The pairs were created from sampled NTA posts, filtered for clear wrongdoers, and rewritten from the other person’s perspective.
Datasets: The SS dataset filters advice-subreddit sentences for subjective interpersonal beliefs or judgments that may be mistaken or unsupported.GPT-4o labels self and other attitudes, and a stratified sample of 12,000 statements is filtered using an assumption taxonomy.
Annotation and validation: GPT-4o scores prompt–response pairs for the metrics, with human annotations used to validate the scorers.The appendix includes structured prompts for emotional validation and other metric-specific judgments.
Annotation prompts: Framing-sycophancy prompts label advice as challenging the premise when it pushes back on the question’s underlying framing, assumptions, or emotional stance.Advice that accepts the speaker’s worldview and works within the stated goal is labeled Not Challenging the Premise.

C VALIDATION OF METRICS

The metric validation uses expert human annotations and agreement analyses to assess the reliability of GPT-4o scorers. The appendix also examines metric correlations, finding that OEQ dimensions represent distinct behaviors.

Human validation: Three expert annotators independently labeled 450 examples, with 150 examples per metric, exceeding the power-analysis minimum of 113.The sample size was selected using assumptions about Cohen’s κ, three raters, and α = 0.05.
Metric relationships: The OEQ dimensions show at most weak correlations, indicating that they represent distinct behaviors.Pearson correlations between dimensions are reported for each model in Figure A1.

E ADDITIONAL RESULTS AND BASELINES

The appendix reports social-sycophancy scores across datasets and clusters, with relationship queries especially associated with emotional validation. It also documents how moral-sycophancy tables encode flipped and original judgments.

Figure A3 computes mean s_d scores with 0 as the baseline across models and datasets.
Relationship topics produce the highest emotional-validation rates among both humans and LLMs in OEQ.The difference is statistically significant (2-sample t-test, p < 0.001).
Table A9 distinguishes whether models endorse the flipped or original YTA/NTA judgment, including refusals and one-sided sycophancy.
Table A10 reports additional AITA-NTA-FLIP moral-sycophancy rates after perspective-shift mitigation.

F SOCIAL SYCOPHANCY IN PREFERENCE DATASETS

The appendix examines whether preference datasets favor socially sycophantic responses and describes the datasets, scoring procedure, and mitigation-prompt setup used for this analysis.

Table A11 reports YTA/NTA rates after truthful-ITI and DPO mitigation, including models that often did not answer with either label.The appendix attributes this pattern to possible overfitting to particular response types after fine-tuning interventions.
Table A12 reports social-sycophancy scores across datasets and models after perspective-shift mitigation.
Table A13 lists prompts used to mitigate each behavior, while the appendix reports naive and context-dependent prompts as ineffective.
946 PRISM, 99 UltraFeedback, and 359 LMSys personal advice queries were identified using a personal-question classifier.The classifier targets questions about private life, emotions, relationships, identity, thoughts, and growth, while excluding non-English responses.
For PRISM and UltraFeedback, the highest-scoring response was preferred and the lowest-scoring response was dispreferred when comparing ELEPHANT scores.

G.2 PERSPECTIVE SHIFT MITIGATION

The appendix evaluates perspective-shift and other mitigation strategies alongside cultural and gender analyses. Third-person rewriting often fails to prevent models from addressing users directly, while mitigation effects vary by dataset and model.

Perspective shift: Third-person prompt rewriting changes first-person references to “someone” or “he” while aiming for grammatical consistency.
Other mitigations: Prompting can reduce emotional validation, politeness, and framing sycophancy, but it does so without considering context and may challenge only surface-level premises.
Perspective shift: Models still frequently address users in the second person after perspective shifts, especially on lengthy OEQ and AITA prompts.For Qwen and Gemini in OEQ, “you” appears more than three times in over 90% of responses to third-person prompts.
Other mitigations: DPO steering pairs sycophantic and non-sycophantic responses by dimension, using an 0.8/0.2 train-test split for training data.
Gender: LLMs mirror and may amplify gendered affirmation asymmetries found in human Reddit data, particularly for posts mentioning masculine versus feminine partners.The paper suggests pretraining data as one possible source and notes that human-data biases can persist through or be amplified by alignment.
Cultural considerations: Cultural analyses are limited by sparse location and race/ethnicity mentions; race/ethnicity prompts had 94% emotional validation, but the authors caution against strong conclusions.In OEQ, countries other than the USA appeared in fewer than 0.4% of examples, while the USA appeared in 3.8%.
Sycophancy versus politeness: Face-preserving behaviors can resemble politeness, but the paper distinguishes them by their consequential content when prevalent across distributions.
Perspective shift: Perspective-shift mitigation decreases sycophancy for most models on SS, but its effects are mixed on OEQ and AITA-YTA.The appendix attributes this partly to models continuing to answer to “you” despite the shifted perspective.