Summary of List Items One by One: a New Data Source and Learning Paradigm For Multimodal Llms, By An Yan et al.
List Items One by One: A New Data Source and Learning Paradigm for Multimodal LLMs
by An Yan, Zhengyuan Yang, Junda Wu, Wanrong Zhu, Jianwei Yang, Linjie Li, Kevin Lin, Jianfeng Wang, Julian McAuley, Jianfeng Gao, Lijuan Wang
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 Set-of-Mark (SoM) Prompting, a technique that enables GPT-4V and other Multimodal Large Language Models (MLLMs) to associate visual objects with text tags inserted on the image. The authors propose a new learning paradigm called “list items one by one,” which asks the model to enumerate and describe all visual tags in alphanumeric order. By integrating their curated dataset with other visual instruction tuning datasets, they equip existing MLLMs with SoM prompting ability. The paper evaluates finetuned SoM models on five MLLM benchmarks, showing significant enhancements in visual reasoning capabilities and reduced hallucinations. Interestingly, these improvements persist even when visual tags are omitted during inference. The authors also conduct analyses to understand the working mechanism of SoM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SoM Prompting helps computers better understand images by letting them know what’s happening in those pictures. It’s like giving a computer a list of things to identify and describe, which makes it really good at recognizing objects. The researchers came up with a new way to teach computers this skill called “list items one by one.” They took existing language models and taught them how to use SoM Prompting by showing them lots of images with text labels. This helped the models get much better at understanding what’s in the pictures. What’s cool is that even when they don’t have the text labels, their models are still really good at recognizing things. |
Keywords
» Artificial intelligence » Gpt » Inference » Instruction tuning » Prompting
