Playing Devil's Advocate: Off-the-Shelf Persona Vectors Rival Targeted Steering for Sycophancy
arXiv:2605.21006v1 Announce Type: cross Abstract: We study the effect of different persona on \textbf{sycophancy}: model's agreement with users even when the user is incorrect. The standard mitigation, Contrastive Activation Addition (CAA), derives a steering direction from labelled pairs of sycophantic and honest responses. This study evaluates whether off-the-shelf persona steering vectors, originally developed for general role-playing and not trained on sycophancy data, can serve as an alternative. In two instruction-tuned models, steering toward personas characterised by doubt or scrutiny
The proliferation of advanced AI models necessitates robust methods to control their behavior and align them with human values, addressing issues like sycophancy that undermine their utility and trustworthiness.
This research demonstrates a more generalized and potentially efficient approach to mitigate AI sycophancy, which is crucial for building reliable AI systems in various applications.
The reliance on specialized sycophancy training data may decrease, potentially simplifying the development and deployment of debiased AI models through the use of 'off-the-shelf' persona steering.
- · AI developers
- · AI ethics research
- · Enterprises deploying AI
- · Malicious actors exploiting AI weaknesses
AI models become less prone to agreeing with incorrect user inputs due to persona-based steering.
The development pipeline for AI alignment and safety features becomes more efficient, leading to faster deployment of robust AI.
Increased public and institutional trust in AI systems due to improved reliability and reduced manipulative tendencies.
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Read at arXiv cs.LG