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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2409.00447 |
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| _version_ | 1866912937727229952 |
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| author | de Rodrigo, I. Sanchez-Cuadrado, A. Boal, J. Lopez-Lopez, A. J. |
| author_facet | de Rodrigo, I. Sanchez-Cuadrado, A. Boal, J. Lopez-Lopez, A. J. |
| contents | This paper introduces the MERIT Dataset, a multimodal (text + image + layout) fully labeled dataset within the context of school reports. Comprising over 400 labels and 33k samples, the MERIT Dataset is a valuable resource for training models in demanding Visually-rich Document Understanding (VrDU) tasks. By its nature (student grade reports), the MERIT Dataset can potentially include biases in a controlled way, making it a valuable tool to benchmark biases induced in Language Models (LLMs). The paper outlines the dataset's generation pipeline and highlights its main features in the textual, visual, layout, and bias domains. To demonstrate the dataset's utility, we present a benchmark with token classification models, showing that the dataset poses a significant challenge even for SOTA models and that these would greatly benefit from including samples from the MERIT Dataset in their pretraining phase. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00447 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The MERIT Dataset: Modelling and Efficiently Rendering Interpretable Transcripts de Rodrigo, I. Sanchez-Cuadrado, A. Boal, J. Lopez-Lopez, A. J. Artificial Intelligence This paper introduces the MERIT Dataset, a multimodal (text + image + layout) fully labeled dataset within the context of school reports. Comprising over 400 labels and 33k samples, the MERIT Dataset is a valuable resource for training models in demanding Visually-rich Document Understanding (VrDU) tasks. By its nature (student grade reports), the MERIT Dataset can potentially include biases in a controlled way, making it a valuable tool to benchmark biases induced in Language Models (LLMs). The paper outlines the dataset's generation pipeline and highlights its main features in the textual, visual, layout, and bias domains. To demonstrate the dataset's utility, we present a benchmark with token classification models, showing that the dataset poses a significant challenge even for SOTA models and that these would greatly benefit from including samples from the MERIT Dataset in their pretraining phase. |
| title | The MERIT Dataset: Modelling and Efficiently Rendering Interpretable Transcripts |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2409.00447 |