Summary of Certainly Uncertain: a Benchmark and Metric For Multimodal Epistemic and Aleatoric Awareness, by Khyathi Raghavi Chandu et al.
Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness
by Khyathi Raghavi Chandu, Linjie Li, Anas Awadalla, Ximing Lu, Jae Sung Park, Jack Hessel, Lijuan Wang, Yejin Choi
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a taxonomy of uncertainty specifically designed for vision-language AI systems. The authors distinguish between epistemic uncertainty (arising from a lack of information) and aleatoric uncertainty (due to inherent unpredictability). They then synthesize a benchmark dataset, CertainlyUncertain, featuring 178K visual question answering (VQA) samples as contrastive pairs. To create this dataset, they inpainted images to make previously answerable questions into unanswerable ones and used image captions to prompt large language models for both answerable and unanswerable questions. Additionally, the authors introduce a new metric, confidence-weighted accuracy, which is well-correlated with both accuracy and calibration error. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps AI systems be more truthful and reliable by understanding different types of uncertainty. It creates a special list of categories that apply just to vision-language AI, where you have images and words working together. They also make a big dataset with lots of examples, called CertainlyUncertain, which has both easy and hard questions. This will help researchers test how well their systems can handle uncertain situations. |
Keywords
» Artificial intelligence » Prompt » Question answering
