Summary of Cross-modal Consistency in Multimodal Large Language Models, by Xiang Zhang et al.
Cross-Modal Consistency in Multimodal Large Language Models
by Xiang Zhang, Senyu Li, Ning Shi, Bradley Hauer, Zijun Wu, Grzegorz Kondrak, Muhammad Abdul-Mageed, Laks V.S. Lakshmanan
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The abstract discusses recent advancements in multimodal methodologies that enable models to process diverse data types, including text, audio, and visual content. Specifically, it highlights the performance of Vision Large Language Models (VLLMs) like GPT-4V, which integrate computer vision with advanced language processing. These models excel at handling intricate tasks requiring simultaneous understanding of textual and visual information. The abstract notes that existing analyses have limitations, focusing on isolated evaluation of each modality’s performance without exploring cross-modal interactions. To address this gap, the study introduces a novel concept called cross-modal consistency and proposes a quantitative evaluation framework. Experimental findings based on curated parallel vision-language datasets reveal inconsistencies between the vision and language modalities within GPT-4V, despite its portrayal as a unified multimodal model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how computers can understand and process different types of data, like text, pictures, and sounds. It talks about special models called Vision Large Language Models (VLLMs) that are good at understanding images and words together. These models are really good at doing tasks that need to look at both the picture and what’s written about it. However, researchers haven’t fully understood how these models work when they’re given different types of data. The study introduces a new way to measure this called cross-modal consistency and shows that even though these models seem like they can handle any type of data, they actually do better with some types than others. |
Keywords
» Artificial intelligence » Gpt
