Summary of Vintern-1b: An Efficient Multimodal Large Language Model For Vietnamese, by Khang T. Doan et al.
Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese
by Khang T. Doan, Bao G. Huynh, Dung T. Hoang, Thuc D. Pham, Nhat H. Pham, Quan T.M. Nguyen, Bang Q. Vo, Suong N. Hoang
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 paper introduces Vintern-1B, a 1-billion-parameter multimodal large language model for Vietnamese tasks. The model combines the Qwen2-0.5B-Instruct language model with InternViT-300M-448px visual model, optimized for OCR, document extraction, and question-answering in Vietnamese context. Fine-tuned on over 3 million image-question-answer pairs, Vintern-1B achieves robust performance across multiple benchmarks like OpenViVQA and ViTextVQA. The model is small enough for on-device applications. The paper also open-sources several Vietnamese vision question answering datasets for text and diagrams. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a big language model called Vintern-1B to help with tasks in the Vietnamese language. It’s like a super smart dictionary that can understand words, pictures, and questions. This model is good at recognizing characters, extracting information from documents, and answering questions about what it sees or reads. The model was trained on lots of examples and works well on tests. It’s also small enough to fit on devices like smartphones. |
Keywords
» Artificial intelligence » Language model » Large language model » Question answering
