Feb. 15, 2024, 5:41 a.m. | Daniel D. Johnson, Daniel Tarlow, David Duvenaud, Chris J. Maddison

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.08733v1 Announce Type: new
Abstract: Identifying how much a model ${\widehat{p}}_{\theta}(Y|X)$ knows about the stochastic real-world process $p(Y|X)$ it was trained on is important to ensure it avoids producing incorrect or "hallucinated" answers or taking unsafe actions. But this is difficult for generative models because probabilistic predictions do not distinguish between per-response noise (aleatoric uncertainty) and lack of knowledge about the process (epistemic uncertainty), and existing epistemic uncertainty quantification techniques tend to be overconfident when the model underfits. We propose …

abstract arxiv cs.lg experts generative generative models predictions process stochastic type world

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