Summary of On Domain-specific Post-training For Multimodal Large Language Models, by Daixuan Cheng et al.
On Domain-Specific Post-Training for Multimodal Large Language Models
by Daixuan Cheng, Shaohan Huang, Ziyu Zhu, Xintong Zhang, Wayne Xin Zhao, Zhongzhi Luan, Bo Dai, Zhenliang Zhang
First submitted to arxiv on: 29 Nov 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 The paper investigates the domain adaptation of general multimodal large language models (MLLMs) through post-training, focusing on data synthesis, training pipelines, and task evaluation. The authors develop a visual instruction synthesizer to generate diverse visual instruction tasks from domain-specific image-caption pairs, which outperforms manual rules, GPT-4, and GPT-4V in enhancing MLLM performance. They also apply a single-stage training pipeline to enhance task diversity for domain-specific post-training. Experiments are conducted on two domains – biomedicine and food – using MLLMs of different sources and scales, evaluating their performance on various domain-specific tasks. The authors will open-source their implementations to support further research in MLLM domain adaptation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make general language models better for specific areas like medicine or food. They want to teach these models new things by giving them more examples and training them in a special way. This helps the models understand what they’re learning is important for that area, not just general knowledge. The authors also create tools to help other researchers work with these models. |
Keywords
» Artificial intelligence » Domain adaptation » Gpt
