Summary of Lapdoc: Layout-aware Prompting For Documents, by Marcel Lamott et al.
LAPDoc: Layout-Aware Prompting for Documents
by Marcel Lamott, Yves-Noel Weweler, Adrian Ulges, Faisal Shafait, Dirk Krechel, Darko Obradovic
First submitted to arxiv on: 15 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 In this paper, researchers investigate whether large language models (LLMs) can be used for document-specific tasks by incorporating layout information into their prompts. Currently, there is a gap in the literature regarding the comparison of purely text-based LLMs and multi-modal document transformers for document understanding tasks. The authors explore two approaches: drop-in modifications and rule-based methods to enrich textual LLM prompts with layout information. Experiments are conducted on the commercial ChatGPT model and the open-source LLM Solar, demonstrating that both models show improved performance on standard document benchmarks when using the proposed approach. Additionally, the paper studies the impact of noisy OCR and layout errors, as well as the limitations of LLMs in utilizing document layout. The 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how big language models can be used to understand documents better. Right now, there are two kinds of models: ones that only use text and ones that also look at the layout of a document. The researchers wanted to know which one is best for certain tasks. They found that by adding layout information to text-only model prompts, both models could do better on standard tests. This means that we might be able to get even more accurate results from these big language models. |
Keywords
* Artificial intelligence * Multi modal
