Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness

• URL: http://arxiv.org/abs/2407.01942v1
• Date: Tue, 2 Jul 2024 04:23:54 GMT
• Title: Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness
• Authors: Khyathi Raghavi Chandu, Linjie Li, Anas Awadalla, Ximing Lu, Jae Sung Park, Jack Hessel, Lijuan Wang, Yejin Choi,
• Abstract summary: We present a taxonomy of uncertainty specific to vision-language AI systems. We also introduce a new metric confidence-weighted accuracy, that is well correlated with both accuracy and calibration error.
• Score: 106.52630978891054
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ability to acknowledge the inevitable uncertainty in their knowledge and reasoning is a prerequisite for AI systems to be truly truthful and reliable. In this paper, we present a taxonomy of uncertainty specific to vision-language AI systems, distinguishing between epistemic uncertainty (arising from a lack of information) and aleatoric uncertainty (due to inherent unpredictability), and further explore finer categories within. Based on this taxonomy, we synthesize a benchmark dataset, CertainlyUncertain, featuring 178K visual question answering (VQA) samples as contrastive pairs. This is achieved by 1) inpainting images to make previously answerable questions into unanswerable ones; and 2) using image captions to prompt large language models for both answerable and unanswerable questions. Additionally, we introduce a new metric confidence-weighted accuracy, that is well correlated with both accuracy and calibration error, to address the shortcomings of existing metrics.

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