Summary of Haloquest: a Visual Hallucination Dataset For Advancing Multimodal Reasoning, by Zhecan Wang et al.
HaloQuest: A Visual Hallucination Dataset for Advancing Multimodal Reasoning
by Zhecan Wang, Garrett Bingham, Adams Yu, Quoc Le, Thang Luong, Golnaz Ghiasi
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
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| 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 introduces HaloQuest, a novel visual question answering dataset designed to evaluate and address multimodal hallucination in vision-language models (VLMs). Hallucination is a critical challenge for VLMs, which must deal with both textual and visual inputs. The dataset captures various aspects of multimodal hallucination, including false premises, insufficient contexts, and visual challenges. To enable large-scale dataset creation, HaloQuest leverages synthetic images alongside real ones. With over 7.7K examples spanning across a wide variety of categories, HaloQuest serves as both a challenging benchmark for VLMs and a fine-tuning dataset for advancing multimodal reasoning. The paper reveals that current models struggle with HaloQuest, achieving below 36% accuracy, but fine-tuning on the dataset significantly reduces hallucination rates while preserving performance on standard reasoning tasks. The results also demonstrate high correlation between generated images and real images (r=0.97) and propose a novel Auto-Eval mechanism highly correlated with human raters (r=0.99). This work makes concrete strides towards understanding, evaluating, and mitigating hallucination in VLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new way to test how well artificial intelligence models can understand images and words together. These models are called vision-language models (VLMs) and they’re really good at answering questions when given both an image and some text. But sometimes these models make mistakes, like saying something isn’t in the image when it actually is. This new dataset, called HaloQuest, helps researchers figure out why this happens and how to make the models better. It’s made up of over 7,700 examples that show different types of images with words, and it’s designed to be a challenge for VLMs. The results show that current models don’t do very well on this dataset, but they can get better if trained on it. This is an important step towards making sure these AI models are reliable. |
Keywords
» Artificial intelligence » Fine tuning » Hallucination » Question answering
