Summary of Better Language Models Exhibit Higher Visual Alignment, by Jona Ruthardt et al.
Better Language Models Exhibit Higher Visual Alignment
by Jona Ruthardt, Gertjan J. Burghouts, Serge Belongie, Yuki M. Asano
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 paper investigates how well text-only Large Language Models (LLMs) naturally align with visual representations. The authors use a discriminative vision-language model framework to analyze frozen text representations and measure zero-shot generalization on unseen classes. The results show that decoder-based LLMs exhibit high intrinsic visual alignment, with more capable models demonstrating stronger generalization. Additionally, the authors find that utilizing frozen LLMs leads to strong gains in cross-lingual settings, surpassing CLIP’s accuracy of 1.4% with 38.7% for Chinese. The proposed method improves both robustness and generalization, reducing the need for paired data and compute. This makes vision-language models more accessible and adaptable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well computer programs that understand text (LLMs) can work with pictures without any extra training. They used a special way to analyze these language models and found that some of them are really good at understanding what’s in a picture, even if they’ve never seen it before. The results show that the better language models are at this task. This means we can use these language models for more things, like translating text from one language to another, without needing as much training data or powerful computers. This makes these programs more useful and easier to use. |
Keywords
» Artificial intelligence » Alignment » Decoder » Generalization » Language model » Zero shot
