Summary of Improving the Efficiency Of Visually Augmented Language Models, by Paula Ontalvilla et al.
Improving the Efficiency of Visually Augmented Language Models
by Paula Ontalvilla, Aitor Ormazabal, Gorka Azkune
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 paper proposes a novel approach to visually augmenting autoregressive Language Models (LMs) without relying on explicit images. Instead, it uses visually-grounded text representations from the CLIP multimodal system. The modified model, named BLIND-VALM, is compared to VALM, which uses image retrieval and representation. Results show that BLIND-VALM performs similarly or even outperforms VALM for Visual Language Understanding (VLU), Natural Language Understanding (NLU) and Language Modeling tasks, while being more efficient and simpler. The paper highlights the potential of this approach for scaling up language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to help computers learn about pictures without needing to see them directly. Currently, language models like LMs are great at understanding words but struggle with visual things. To fix this, some people use special image-retrieval systems or generate fake images. But the researchers in this paper found a better way: they used text that is connected to pictures from something called CLIP. They took this text and put it into an existing language model, called VALM, and made it work with the new “picture” text. This new model, called BLIND-VALM, does just as well or even better than the old one at understanding words and pictures. |
Keywords
» Artificial intelligence » Autoregressive » Language model » Language understanding
