用概率方法捕捉生成模型的多重风险，让安全评估更全面可信。
Embracing Ambiguity: Bayesian Nonparametrics and Stakeholder Participation for Ambiguity-Aware Safety Evaluation
- 构建解码参数空间的多模态风险分布，量化不同配置下的潜在危害
- 通过贝叶斯非参数模型发现多个近优操作区域，揭示风险多样性
- 融合利益相关方偏好，支持动态探索与可解释的安全评估
生成式AI模型的安全评估常将复杂行为简化为单一数值，仅基于一种解码配置，导致尾部风险、群体差异及多个近优操作点被掩盖。本文提出统一框架，通过建模解码参数与提示词空间中危害行为的分布，以尾部聚焦指标量化风险，并整合利益相关方偏好。技术贡献包括：(i) 定义解码拉什蒙集合，即在给定标准下风险近优的参数区域，测量其大小与分歧度；(ii) 设计受利益相关方条件影响的依赖狄利克雷过程混合模型，学习多模态危害表面；(iii) 提出基于贝叶斯深度学习代理的主动采样流程，高效探索参数空间。该方法融合多重性理论、贝叶斯非参数统计与利益相关方对齐的敏感性分析，推动生成模型的可信部署。
原文摘要 · Abstract (English)
Evaluations of generative AI models often collapse nuanced behaviour into a single number computed for a single decoding configuration. Such point estimates obscure tail risks, demographic disparities, and the existence of multiple near-optimal operating points. We propose a unified framework that embraces multiplicity by modelling the distribution of harmful behaviour across the entire space of decoding knobs and prompts, quantifying risk through tail-focused metrics, and integrating stakeholder preferences. Our technical contributions are threefold: (i) we formalise decoding Rashomon sets, regions of knob space whose risk is near-optimal under given criteria and measure their size and disagreement; (ii) we develop a dependent Dirichlet process (DDP) mixture with stakeholder-conditioned stick-breaking weights to learn multi-modal harm surfaces; and (iii) we introduce an active sampling pipeline that uses Bayesian deep learning surrogates to explore knob space efficiently. Our approach bridges multiplicity theory, Bayesian nonparametrics, and stakeholder-aligned sensitivity analysis, paving the way for trustworthy deployment of generative models.
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