Summary of Automatically Generating Visual Hallucination Test Cases For Multimodal Large Language Models, by Zhongye Liu et al.
Automatically Generating Visual Hallucination Test Cases for Multimodal Large Language Models
by Zhongye Liu, Hongbin Liu, Yuepeng Hu, Zedian Shao, Neil Zhenqiang Gong
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 VHExpansion, the first automated method for expanding visual hallucination (VH) test cases for multimodal large language models (MLLMs). The existing methods rely on human annotations and are limited by their small size. VHExpansion expands initial test cases by perturbing questions and answers through negation, as well as modifying images using common and adversarial perturbations. The authors propose a new evaluation metric called symmetric accuracy, which measures the proportion of correctly answered VH test-case pairs, including negated counterparts. This unbiased metric is less susceptible to imbalance in testing cases when an MLLM randomly guesses answers. The paper applies VHExpansion to three manually annotated VH datasets and benchmarks seven MLLMs using these expanded datasets. The results show that VHExpansion effectively identifies more VH test cases, and the symmetric accuracy metric leads to different conclusions about the vulnerability of MLLMs to VH compared to traditional accuracy metrics. Fine-tuning MLLMs on the expanded dataset generated by VHExpansion mitigates VH more effectively than fine-tuning on the original manually annotated dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that language models don’t make up things they didn’t learn. Sometimes, these models can generate fake information, especially when it comes to images and what’s described in them. The authors created a new way to test language models for this kind of mistake by automatically generating more test cases. They also came up with a new way to measure how well the models do on these tests called symmetric accuracy. This method is better than previous ones because it doesn’t get fooled by the models randomly guessing answers. The researchers tested their new method on three datasets and found that it’s more effective at catching language models when they make mistakes. They also showed that fine-tuning the models to correct these mistakes makes them perform even better. |
Keywords
» Artificial intelligence » Fine tuning » Hallucination
