ETN is a lightweight post-hoc module that applies a learned sample-dependent affine transformation to pretrained model logits and interprets the outputs as Dirichlet parameters to enable efficient uncertainty estimation.
Bayesian low-rank adaptation for large language models
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Evidential Transformation Network: Turning Pretrained Models into Evidential Models for Post-hoc Uncertainty Estimation
ETN is a lightweight post-hoc module that applies a learned sample-dependent affine transformation to pretrained model logits and interprets the outputs as Dirichlet parameters to enable efficient uncertainty estimation.