Summary of Vl-icl Bench: the Devil in the Details Of Multimodal In-context Learning, by Yongshuo Zong et al.
VL-ICL Bench: The Devil in the Details of Multimodal In-Context Learning
by Yongshuo Zong, Ondrej Bohdal, Timothy Hospedales
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This study investigates the capabilities and limitations of multimodal in-context learning (ICL) using large language models (LLMs). Built on top of LLMs, vision large language models (VLLMs) have made significant progress in areas such as recognition, reasoning, and grounding. However, existing research has primarily focused on few-shot visual question answering (VQA) and image captioning, which do not fully exploit the strengths of ICL or test its limitations. The authors introduce a comprehensive benchmark VL-ICL Bench for multimodal ICL, encompassing various tasks that involve both images and text as inputs and outputs, and different types of challenges. They evaluate state-of-the-art VLLMs against this benchmark suite, revealing their diverse strengths and weaknesses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how large language models can learn new things when shown a few examples. These models are good at recognizing pictures and understanding words. Researchers have been testing these models on simple tasks like answering questions about pictures or writing captions for them. But the authors of this paper want to know what else these models can do. They created a special test called VL-ICL Bench that shows how well the models do on many different tasks. The tests include things like recognizing objects in pictures, understanding natural language, and using context to figure out the meaning of words. |
Keywords
* Artificial intelligence * Few shot * Grounding * Image captioning * Question answering
