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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.15211 |
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Table of Contents:
- 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.