Estimating Uncertainty in Multimodal Foundation Models using Public Internet Data

• URL: http://arxiv.org/abs/2310.09926v2
• Date: Sun, 26 Nov 2023 05:54:48 GMT
• Title: Estimating Uncertainty in Multimodal Foundation Models using Public Internet Data
• Authors: Shiladitya Dutta, Hongbo Wei, Lars van der Laan, Ahmed M. Alaa
• Abstract summary: Foundation models are trained on vast amounts of data at scale using self-supervised learning. In this paper, we address the problem of quantifying uncertainty in zero-shot predictions. We propose a approach for uncertainty estimation in zero-shot settings using conformal prediction with web data.
• Score: 15.365603519829088
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Foundation models are trained on vast amounts of data at scale using self-supervised learning, enabling adaptation to a wide range of downstream tasks. At test time, these models exhibit zero-shot capabilities through which they can classify previously unseen (user-specified) categories. In this paper, we address the problem of quantifying uncertainty in these zero-shot predictions. We propose a heuristic approach for uncertainty estimation in zero-shot settings using conformal prediction with web data. Given a set of classes at test time, we conduct zero-shot classification with CLIP-style models using a prompt template, e.g., "an image of a <category>", and use the same template as a search query to source calibration data from the open web. Given a web-based calibration set, we apply conformal prediction with a novel conformity score that accounts for potential errors in retrieved web data. We evaluate the utility of our proposed method in Biomedical foundation models; our preliminary results show that web-based conformal prediction sets achieve the target coverage with satisfactory efficiency on a variety of biomedical datasets.

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