Research map / Large language models / Alignment and preferences
When Helpfulness Becomes Sycophancy: Sycophancy is a Boundary Failure Between Social Alignment and Epistemic Integrity in Large Language Models
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Position paper arguing sycophancy is a boundary failure between social alignment and epistemic integrity, proposing a three-condition decision rule, a taxonomy (targets, mechanisms, severity), and implications for boundary-aware evaluation and mitigation.
Problem
Existing sycophancy definitions and evaluations operationalize it through overt agreement, reversals, or factual error, missing subtle forms that overlap with legitimate social behavior such as empathy and rapport.
Approach
Defines sycophancy as behavior that prioritizes affirming a user's beliefs, preferences, or self-concept in a way that reduces epistemic integrity, requiring three conjunctive conditions (user cue, alignment shift, normative degradation), plus a taxonomy of alignment targets, mechanisms, and severity.
Key results
- Proposes a three-condition decision rule (user cue, alignment shift, normative degradation) where all three must hold for sycophancy.
- Introduces a taxonomy with three alignment targets (informational, cognitive, affective), four mechanisms (explicit answer alignment, premise endorsement, affective over-alignment, stance instability), and a three-level severity scale.
- Illustrates the framework with three qualitative ChatGPT 4.1 excerpts (Cases A-C), classifying A and C as sycophantic and B as not; no quantitative evaluation is reported.
Limitations
- No empirical validation of the framework, taxonomy, or severity scale; illustrations are three qualitative cases.
- Severity and normative degradation judgments rely on subjective criteria (e.g., 'knowledgeable, honest, objective advisor') that may be hard to operationalize consistently.
- Proposed rubrics and mitigation strategies are described at a high level without concrete implementations.
Experimental setup
Builds on: Du et al. sycophancy typology ELEPHANT Social Sycophancy Scale
Closest papers by meaning
- Towards Understanding Sycophancy in Language Models (ICLR 2024 · similarity 0.88)
- Real-Time Monitoring and Calibration of Chain-of-Thought Sycophancy in Large Reasoning Models (ICML 2026 · similarity 0.85)
- How do Large Language Models Navigate Conflicts between Honesty and Helpfulness? (ICML 2024 · similarity 0.85)
- Uncovering Gaps in How Humans and LLMs Interpret Subjective Language (ICLR 2025 · similarity 0.85)
- Have the VLMs Lost Confidence? A Study of Sycophancy in VLMs (ICLR 2025 · similarity 0.85)
- Alignment for Honesty (NeurIPS 2024 · similarity 0.84)
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