Summary of The Merit Dataset: Modelling and Efficiently Rendering Interpretable Transcripts, by I. De Rodrigo et al.
The MERIT Dataset: Modelling and Efficiently Rendering Interpretable Transcripts
by I. de Rodrigo, A. Sanchez-Cuadrado, J. Boal, A. J. Lopez-Lopez
First submitted to arxiv on: 31 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 introduces the MERIT Dataset, a multimodal dataset consisting of text, images, and layouts, fully labeled with over 400 labels and 33k samples. This dataset is specifically designed for training models in Visually-rich Document Understanding (VrDU) tasks. As school reports, the dataset can potentially include controlled biases, making it valuable for benchmarking bias induction in Language Models (LLMs). The paper outlines the generation pipeline and highlights its features in textual, visual, layout, and bias domains. To demonstrate its utility, a benchmark is presented with token classification models, showing that even SOTA models struggle with the dataset and would benefit from including MERIT samples in their pretraining phase. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special kind of dataset called MERIT, which has text, pictures, and layouts. It’s very useful for training computers to understand documents better. The dataset is made up of over 33,000 examples, each with multiple labels. This makes it perfect for testing how well language models can handle biases in the data they’re trained on. The paper explains how the dataset was created and shows that even the best models have trouble understanding some parts of it. This means that including more samples from MERIT in model training could make them even better. |
Keywords
» Artificial intelligence » Classification » Pretraining » Token
