Summary of Lavy: Vietnamese Multimodal Large Language Model, by Chi Tran and Huong Le Thanh
LaVy: Vietnamese Multimodal Large Language Model
by Chi Tran, Huong Le Thanh
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); 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 Large Language Models (LLMs) and Multimodal Large Language models (MLLMs) have achieved impressive capabilities in complex reasoning and linguistic comprehension. However, the development of Vietnamese MLLMs is hindered by a lack of high-quality multimodal resources. This paper addresses this issue by introducing LaVy, a state-of-the-art Vietnamese MLLM, as well as LaVy-Bench, a benchmark designed for evaluating MLLMs’ understanding on Vietnamese visual language tasks. The project is publicly available at this URL. This work pioneers in addressing the limitations of Vietnamese MLLMs and paves the way for further advancements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at understanding Vietnamese language. It’s like teaching a robot to read and understand Vietnamese signs, pictures, and words. Currently, there aren’t many good resources to help with this task. The researchers introduce LaVy, a new tool that can do this job well, along with a special test to see how good it is. This project is important because it can help us make computers smarter and more helpful for Vietnamese people. |
