Summary of Multimodal Reasoning with Multimodal Knowledge Graph, by Junlin Lee and Yequan Wang and Jing Li and Min Zhang
Multimodal Reasoning with Multimodal Knowledge Graph
by Junlin Lee, Yequan Wang, Jing Li, Min Zhang
First submitted to arxiv on: 4 Jun 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 proposed Multimodal Reasoning with Multimodal Knowledge Graph (MR-MKG) method leverages multimodal knowledge graphs (MMKGs) to learn rich, semantic knowledge across modalities, enhancing the multimodal reasoning capabilities of large language models (LLMs). The approach combines relation graph attention networks for encoding MMKGs and cross-modal alignment modules for optimizing image-text alignment. A MMKG-grounded dataset is constructed for pretraining LLMs in multimodal reasoning tasks. Experimental results show that MR-MKG outperforms previous state-of-the-art models on multimodal question answering and analogy reasoning tasks, with only a fraction of the LLM’s parameters trained. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multimodal reasoning with big language models can be tricky because they sometimes make things up or have outdated information. Some people tried to fix this by using special databases, but these only work for one type of information at a time. This paper proposes a new way to learn from many types of information at once. They call it Multimodal Reasoning with Multimodal Knowledge Graph (MR-MKG). It uses a special kind of map that connects different types of information together, and it makes the language model smarter as a result. The results show that this new method works better than previous methods on certain tasks. |
Keywords
* Artificial intelligence * Alignment * Attention * Knowledge graph * Language model * Pretraining * Question answering
