Summary of On the Out-of-distribution Generalization Of Multimodal Large Language Models, by Xingxuan Zhang et al.
On the Out-Of-Distribution Generalization of Multimodal Large Language Models
by Xingxuan Zhang, Jiansheng Li, Wenjing Chu, Junjia Hai, Renzhe Xu, Yuqing Yang, Shikai Guan, Jiazheng Xu, Peng Cui
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 research investigates the limitations of current Multimodal Large Language Models (MLLMs) when applied to out-of-distribution scenarios. The study evaluates the models’ zero-shot generalization capabilities across various synthetic images, real-world distributional shifts, and specialized datasets such as medical and molecular imagery. The results indicate that MLLMs struggle with generalization beyond common training domains, highlighting the need for adaptation techniques. The research identifies mapping deficiency as the primary cause of unreliable performance and demonstrates how in-context learning (ICL) can significantly enhance MLLMs’ generalization capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows that current Multimodal Large Language Models are not very good at applying what they’ve learned to new situations or things they haven’t seen before. Researchers tested these models on different types of images, real-world scenarios, and special datasets like medical pictures. They found that the models don’t work well when they’re used in a way that’s different from how they were trained. The main problem is that the models aren’t good at figuring out what things mean or recognizing important features. This study shows that if you teach the models more about specific situations, they can do better with new tasks. |
Keywords
» Artificial intelligence » Generalization » Zero shot
