Summary of Logra-med: Long Context Multi-graph Alignment For Medical Vision-language Model, by Duy M. H. Nguyen et al.
LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model
by Duy M. H. Nguyen, Nghiem T. Diep, Trung Q. Nguyen, Hoang-Bao Le, Tai Nguyen, Tien Nguyen, TrungTin Nguyen, Nhat Ho, Pengtao Xie, Roger Wattenhofer, James Zhou, Daniel Sonntag, Mathias Niepert
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 LoGra-Med algorithm addresses the limitation of medical multi-modal large language models (med-MLLMs) by enforcing triplet correlations across image modalities, conversation-based descriptions, and extended captions. This approach helps capture contextual meaning, handle linguistic variability, and build cross-modal associations between visuals and text. The algorithm is designed to be efficient, using black-box gradient estimation to enable faster training of large models like LLaMa 7B. Experimental results show that LoGra-Med matches the performance of LLAVA-Med on Medical VQA tasks and outperforms it when trained on a smaller dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical multi-modal large language models (med-MLLMs) are used for various applications, including visual question answering and chatbots. However, these models rely heavily on pre-training datasets, which can be time-consuming to create. The proposed LoGra-Med algorithm improves the performance of med-MLLMs by aligning image modalities with conversation-based descriptions and extended captions. This approach helps the model understand the context and relationships between different types of data. |
Keywords
» Artificial intelligence » Llama » Multi modal » Question answering
