LAPDoc: Layout-Aware Prompting for Documents
Marcel Lamott Yves-Noel Weweler Adrian Ulges Faisal Shafait Dirk Krechel Darko Obradovic
Recent advances in training large language models (LLMs) using massive
amounts of solely textual data lead to strong generalization across many
domains and tasks, including document-specific tasks. Opposed to that there is
a trend to train multi-modal transformer architectures tailored for document
understanding that are designed specifically to fuse textual inputs with the
corresponding document layout. This involves a separate fine-tuning step for
which additional training data is required. At present, no document
transformers with comparable generalization to LLMs are available That raises
the question which type of model is to be preferred for document understanding
tasks. In this paper we investigate the possibility to use purely text-based
LLMs for document-specific tasks by using layout enrichment. We explore drop-in
modifications and rule-based methods to enrich purely textual LLM prompts with
layout information. In our experiments we investigate the effects on the
commercial ChatGPT model and the open-source LLM Solar. We demonstrate that
using our approach both LLMs show improved performance on various standard
document benchmarks. In addition, we study the impact of noisy OCR and layout
errors, as well as the limitations of LLMs when it comes to utilizing document
layout. Our results indicate that layout enrichment can improve the performance
of purely text-based LLMs for document understanding by up to 15% compared to
just using plain document text. In conclusion, this approach should be
considered for the best model choice between text-based LLM or multi-modal
document transformers.