Abstract
This paper proposes LayoutLLM, a more flexible document analysis method for understanding imaged documents. Visually Rich Document Understanding tasks, such as document image classification and information extraction, have gained significant attention due to their importance. Existing methods have been developed to enhance document comprehension by incorporating pre-training awareness of images, text, and layout structure. However, these methods require fine-tuning for each task and dataset, and the models are expensive to train and operate. To overcome this limitation, we propose a new LayoutLLM that integrates these with large-scale language models (LLMs). By leveraging the strengths of existing research in document image understanding and LLMs’ superior language understanding capabilities, the proposed model, fine-tuned with multimodal instruction datasets, performs an understanding of document images in a single model. Our experiments demonstrate improvement over the baseline model in various document analysis tasks.- Anthology ID:
- 2024.lrec-main.892
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 10219–10224
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.892
- DOI:
- Cite (ACL):
- Masato Fujitake. 2024. LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 10219–10224, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding (Fujitake, LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2024.lrec-main.892.pdf