A Mechanistic View of Authority Hierarchy in LLM Sycophancy

Emil Joswin, Srujananjali Medicherla, Priyanka Mary Mammen

Keywords: Interpretability for AI Safety, Methods (probing, steering, causal interventions), Interpretability for Knowledge Discovery

TL;DR: We show that authority-induced sycophancy is not a surface-level output bias but mechanistic knowledge erasure, a precise, layer-localized overwriting of correct internal representations by high-status authority signals.

Abstract:

Authority bias poses a critical safety concern in language models: models systematically prioritize social cues from authority figures over factual consistency, swaying their answers based on source credibility rather than evidence.
We mechanistically investigate this phenomenon using
a controlled medical QA setting, where hints suggesting incorrect answers are attributed to personas of varying expertise. Across Llama-3.1-8B, Qwen3-8B, and Gemma-2-9B, we find that models respond in a graded manner proportional to perceived authority, a hierarchy that is never explicitly prompted but emerges from training. Logit lens analysis and linear/non-linear probing localize this effect
to a critical late layer where correct answer representations are actively erased, an erasure that scales with authority level, resists mean vector intervention, and is only partially reversible through chain-of-thought reasoning. Our findings suggest that authority-induced sycophancy is not a surface-level output bias but mechanistic knowledge erasure, a precise, layer-localized
overwriting of correct internal representations by high-status authority signals.

Submission Number: 746