Summary of Mm-instruct: Generated Visual Instructions For Large Multimodal Model Alignment, by Jihao Liu and Xin Huang and Jinliang Zheng and Boxiao Liu and Jia Wang and Osamu Yoshie and Yu Liu and Hongsheng Li
MM-Instruct: Generated Visual Instructions for Large Multimodal Model Alignment
by Jihao Liu, Xin Huang, Jinliang Zheng, Boxiao Liu, Jia Wang, Osamu Yoshie, Yu Liu, Hongsheng Li
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 introduces MM-Instruct, a large-scale dataset of visual instruction data designed to enhance the instruction-following capabilities of large multimodal models (LMMs). Existing datasets focus on question-answering and struggle to generalize to broader application scenarios. The novel approach constructs MM-Instruct by leveraging existing LLMs to generate diverse instructions from conventional image captioning datasets, matching them with images, and using an open-sourced LLM to generate coherent answers. A benchmark is introduced to evaluate the instruction-following capabilities of existing LMMs. Training a LLaVA-1.5 model on the generated data (LLaVA-Instruct) exhibits significant improvements in instruction-following capabilities compared to LLaVA-1.5 models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a big dataset called MM-Instruct that helps machines learn how to follow instructions better. Right now, most datasets only focus on answering questions, but this one wants to help machines do lots of other things too, like write stories or analyze pictures. To make this happen, the researchers used special computer programs to generate lots of new instructions from old ones and matched them with pictures. They then used another program to come up with good answers to these instructions. The paper also comes with a test to see how well machines do at following instructions. By training a machine learning model on this dataset, it gets way better at following instructions! |
Keywords
» Artificial intelligence » Image captioning » Machine learning » Question answering
