Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness
<|reference_start|>Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness: 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.<|reference_end|>
@article{chandu2024certainly,
    title={Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and
  Aleatoric Awareness},
    author={Khyathi Raghavi Chandu, Linjie Li, Anas Awadalla, Ximing Lu, Jae Sung
  Park, Jack Hessel, Lijuan Wang, Yejin Choi},
    journal={arXiv preprint arXiv:2407.01942},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01942},
    primaryClass={cs.AI cs.CL cs.CV}
}