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arXiv · 2407.01942

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

Khyathi Raghavi Chandu·Linjie Li·Anas Awadalla·Ximing Lu·Jae Sung Park·Jack Hessel·Lijuan Wang·Yejin Choi
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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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Khyathi Raghavi Chandu, Linjie Li, Anas Awadalla, Ximing Lu, Jae Sung Park, Jack Hessel, Lijuan Wang, Yejin Choi. 2024-07-02. Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness. https://arxiv.org/abs/2407.01942

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