Dual Optimal: Make Your LLM Peer-like with Dignity
Current aligned language models exhibit a dual failure mode we term the Evasive Servant: they sycophantically validate flawed user beliefs while deflecting responsibility with boilerplate disclaimers. We propose the Dignified Peer framework, which counters servility with anti-sycophancy and trustworthiness, and mitigates evasiveness through empathy and creativity. Realizing this agent requires overcoming significant challenges in data supervision, objective collapse, and evaluation bias. We address these issues by introducing the PersonaKnob dataset which features a compositional partial order structure of multiple persona preference. This data is utilized alongside a tolerant constrained...
| Authors | Xiangqi Wang, Yue Huang, Haomin Zhuang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 1 April 2026 |
| Last updated at source | 2 April 2026 |
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