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In-Person Poster presentation / poster accept

Calibrating Transformers via Sparse Gaussian Processes

Wenlong Chen · Yingzhen Li

MH1-2-3-4 #121

Keywords: [ bayesian neural networks ] [ variational inference ] [ uncertainty estimation ] [ transformers ] [ gaussian processes ] [ Probabilistic Methods ]


Transformer models have achieved profound success in prediction tasks in a wide range of applications in natural language processing, speech recognition and computer vision. Extending Transformer’s success to safety-critical domains requires calibrated uncertainty estimation which remains under-explored. To address this, we propose Sparse Gaussian Process attention (SGPA), which performs Bayesian inference directly in the output space of multi-head attention blocks (MHAs) in transformer to calibrate its uncertainty. It replaces the scaled dot-product operation with a valid symmetric kernel and uses sparse Gaussian processes (SGP) techniques to approximate the posterior processes of MHA outputs. Empirically, on a suite of prediction tasks on text, images and graphs, SGPA-based Transformers achieve competitive predictive accuracy, while noticeably improving both in-distribution calibration and out-of-distribution robustness and detection.

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