Summary of Improving Language Understanding From Screenshots, by Tianyu Gao et al.
Improving Language Understanding from Screenshots
by Tianyu Gao, Zirui Wang, Adithya Bhaskar, Danqi Chen
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a novel approach to improving language understanding in screenshot language models (LMs), which process both text and images within a single visual view. The authors focus on enhancing the text abilities of these models, which are crucial for tasks such as chart understanding and UI navigation. They introduce a Patch-and-Text Prediction (PTP) objective that masks and recovers image patches and text within screenshots. The proposed method achieves comparable performance with BERT on 6 out of 8 GLUE tasks and improves up to 8% over prior work. Additionally, the authors extend PTP to train autoregressive screenshot LMs, which significantly reduce perplexity by utilizing screenshot context. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about making computers better at understanding pictures with text in them. Right now, these models are not as good as others that only look at words. The scientists found a new way to make the picture-text models learn faster and more accurately. They used special tricks like hiding parts of the image and recovering them again. This helped their model get closer to the best ones. They also made it so the model could predict what comes next in a sequence, which is useful for many tasks. |
Keywords
* Artificial intelligence * Autoregressive * Bert * Language understanding * Perplexity
