LayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understanding

LayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understanding
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Original Paper
 
Abstract:
Recently, leveraging large language models (LLMs) or multimodal large language models (MLLMs) for document understanding has been proven very promising. However, previous works that employ LLMs/MLLMs for document understanding have not fully explored and utilized the document layout information, which is vital for precise document understanding. In this paper, we propose LayoutLLM, an LLM/MLLM based method for document understanding. The core of LayoutLLM is a layout instruction tuning strategy, which is specially designed to enhance the comprehension and utilization of document layouts. The proposed layout instruction tuning strategy consists of two components: Layout-aware Pre-training and Layout-aware Supervised Fine-tuning. To capture the characteristics of document layout in Layout-aware Pre-training, three groups of pre-training tasks, corresponding to document-level, region-level and segment-level information, are introduced. Furthermore, a novel module called layout chain-of-thought (LayoutCoT) is devised to enable LayoutLLM to focus on regions relevant to the question and generate accurate answers. LayoutCoT is effective for boosting the performance of document understanding. Meanwhile, it brings a certain degree of interpretability, which could facilitate manual inspection and correction. Experiments on standard benchmarks show that the proposed LayoutLLM significantly outperforms existing methods that adopt open-source 7B LLMs/MLLMs for document understanding. The training data of the LayoutLLM is publicly available at
 

Summary Notes

Enhancing Document Understanding with LayoutLLM

As AI technology progresses, understanding documents accurately is increasingly essential for AI Engineers in large enterprises.
Traditional methods have somewhat overlooked the significance of document layout, focusing more on text.
LayoutLLM introduces a novel approach by combining layout-focused tuning with Large Language Models (LLMs) to improve document understanding dramatically.

The Role of Document Layout in AI

The layout of a document provides vital context that goes beyond the text, influencing how information is interpreted.
While models like LayoutLM have started to consider spatial information, most LLMs, including well-known ones like ChatGPT, still often miss out on the insights that layouts provide.

Introducing LayoutLLM

LayoutLLM is a leap forward, integrating layout-aware techniques into the training of LLMs to enhance document understanding. It incorporates:
  • Layout-aware Pre-training: This prepares the model to recognize and interpret document layouts at various levels, enhancing its understanding of layout nuances.
  • Layout-aware Supervised Fine-Tuning (SFT): Using a layout chain-of-thought (LayoutCoT) module, the model learns to focus on important document areas, leading to more accurate responses.

Testing and Results

LayoutLLM's performance shines when tested against standard benchmarks, outperforming existing methods significantly.
These results highlight the value of focusing on layout information to improve document understanding tasks.

Key Contributions of LayoutLLM

LayoutLLM stands out by offering:
  1. Targeted Pre-training Tasks: These tasks help the model grasp layout information thoroughly, from the general structure to specific segments.
  1. LayoutCoT Strategy: Applied during fine-tuning, this strategy improves focus on relevant document areas, enhancing accuracy and making the model's reasoning clearer.
  1. Proven Effectiveness: Extensive testing has confirmed LayoutLLM's superiority in zero-shot document understanding tasks.

Why LayoutLLM Matters

LayoutLLM tackles the challenge of integrating layout information into document AI, offering significant benefits for enterprise applications.
Its approach not only increases accuracy but also makes the model's decision-making process more transparent.
Enterprises could see marked improvements in efficiency and precision in document-related tasks by adopting LayoutLLM.

Further Reading

For a deeper dive into LayoutLLM, supplementary materials provide in-depth information on datasets, training, evaluation, and detailed results.
These resources showcase the thorough development and validation behind LayoutLLM, highlighting its potential as a game-changer in document understanding.
In summary, LayoutLLM sets a new benchmark in document AI by effectively utilizing layout information, promising enhanced capabilities and paving the way for future advancements in the field.

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