Summary of Allava: Harnessing Gpt4v-synthesized Data For Lite Vision-language Models, by Guiming Hardy Chen et al.
ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Models
by Guiming Hardy Chen, Shunian Chen, Ruifei Zhang, Junying Chen, Xiangbo Wu, Zhiyi Zhang, Zhihong Chen, Jianquan Li, Xiang Wan, Benyou Wang
First submitted to arxiv on: 18 Feb 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 study aims to bridge the performance gap between traditional-scale large vision-language models (LVLMs) and resource-friendly lite versions. By leveraging high-quality training data, a comprehensive pipeline is developed to generate a synthetic dataset that can be used to train lite VLMs. The key idea is to utilize strong proprietary models to create fine-grained image annotations for vision-language alignment and complex reasoning visual question-answering pairs for visual instruction fine-tuning. Experimental results demonstrate the effectiveness of the proposed scheme, achieving competitive performance on 17 benchmarks with 4B LVLMs, and even performing on par with 7B/13B-scale models on various benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows that it’s possible to make large vision-language models more efficient by using high-quality training data. The researchers created a special dataset with lots of samples that can be used to train smaller versions of these models. They tested their idea and found that the smaller models performed just as well as the bigger ones on many tasks. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Question answering
