Summary of Seeing Syntax: Uncovering Syntactic Learning Limitations in Vision-language Models, by Sri Harsha Dumpala et al.
Seeing Syntax: Uncovering Syntactic Learning Limitations in Vision-Language Models
by Sri Harsha Dumpala, David Arps, Sageev Oore, Laura Kallmeyer, Hassan Sajjad
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 analyzes the ability of vision-language models (VLMs) to encode syntactic information, a fundamental aspect of language understanding. VLMs are used as foundation models for multi-modal applications such as image captioning and text-to-image generation. Recent studies have highlighted limitations in VLM text encoders, particularly in areas like compositionality and semantic understanding. The study compares VLMs with different objective functions, parameter size, and training data size to uni-modal language models (ULMs) to determine their ability to encode syntactic knowledge. The findings suggest that ULM text encoders acquire syntactic information more effectively than those in VLMs. The paper also investigates the factors that shape the syntactic information learned by VLM text encoders, finding that the pre-training objective plays a crucial role. The performance of different models is analyzed layer-wise, with CLIP showing a drop in performance across layers and other models exhibiting rich syntactic knowledge in middle layers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well vision-language models can understand the structure of language. These models are used to generate text and images together, but they have some limitations. The researchers want to know why these models struggle with certain aspects of language, like understanding complex sentences. They compare different versions of the model to see which one does better. Surprisingly, they find that simpler language models do a better job of understanding how words fit together in a sentence. The study also shows that the way the model is trained makes a big difference in its ability to understand language. |
Keywords
» Artificial intelligence » Image captioning » Image generation » Language understanding » Multi modal
