Best LLMs for Text Layout Classification

Top Performing Models

1. LayoutLMv3
2. UDOP (Unified Document and OCR Pre-training)

These models have shown superior performance in document layout analysis tasks, including text role classification in scientific charts. (Kim et al., 2024)

LayoutLMv3

• Multimodal transformer model
• Uses RoBERTa tokenizer for text embeddings
• Employs DiT for image embeddings
• Incorporates 1D and 2D position embeddings
• Pretraining objectives: MLM, MIM, and WPA
• 12-layer transformer encoder with 12 self-attention heads
• Hidden size of 768 and feed-forward network with 3,072 hidden units

(Kim et al., 2024)

UDOP

• Sequence-to-sequence generative transformer
• Uses layout-induced vision-text embedding
• Single encoder for multimodal input
• Two decoders: one for vision, one for text-layout
• Calculates joint representation of image patches containing text

(Kim et al., 2024)

Performance Comparison

LayoutLMv3 Advantages

• Outperforms UDOP in text role classification tasks
• Achieves highest F1-macro score of 82.87 and F1-micro score of 93.99 on ICPR22 dataset
• More robust to noise compared to UDOP
• Generalizes better across different datasets (CHIME-R, DeGruyter, EconBiz)

(Kim et al., 2024)

UDOP Performance

• Generally performs well but is outperformed by LayoutLMv3
• Shows improvement with data augmentation and balancing methods
• Achieves better results with increased training steps (up to 100,000)

(Kim et al., 2024)

Factors Influencing Performance

Data Augmentation and Balancing

• Improves model robustness, especially for UDOP
• Minor improvement for LayoutLMv3's F1-micro score
• Helps address imbalanced datasets

(Kim et al., 2024)

Dataset Complexity

• Performance varies across datasets (e.g., CHIME-R, DeGruyter, EconBiz)
• DeGruyter and EconBiz pose challenges for text role classification
• Chart type distribution within datasets may affect generalizability

(Kim et al., 2024)

Pretraining Objectives

• LayoutLMv3's Word-Patch Alignment (WPA) may contribute to better performance
• Pretraining on non-chart datasets still yields good results for chart analysis

(Kim et al., 2024)

Emerging Approaches

LayoutLLM

• Combines advantages of VrDU models and Large Language Models (LLMs)
• Uses document layout understanding model as encoder
• Employs LLMs as decoder for language understanding
• Flexible performance across multiple tasks
• Outperforms professionally tuned models in various VrDU tasks

(Fujitake, 2024)

LLM-based Text Enrichment

• Utilizes Large Language Models (e.g., ChatGPT 3.5) to enrich and rewrite input text
• Aims to provide additional context and correct inaccuracies
• Shows promising results in improving embedding performance
• Particularly effective in certain domains (e.g., TwitterSemEval 2015 dataset)

(Harris et al., 2024)

Considerations for Choosing the Best LLM

Task-Specific Requirements

• Consider the specific text layout classification task at hand
• Evaluate model performance on relevant datasets and metrics
• Assess the need for multimodal input processing (text, image, layout)

Computational Resources

• Consider model size and computational requirements
• Evaluate trade-offs between performance and efficiency
• Assess the availability of hardware resources (e.g., GPUs)

Domain Adaptability

• Consider the model's ability to generalize across different document types
• Evaluate performance on domain-specific datasets
• Assess the need for further fine-tuning or domain adaptation

Future Developments

• Stay informed about emerging models and techniques
• Consider the potential for integrating LLMs with specialized layout understanding models
• Evaluate the impact of larger model sizes and improved pretraining techniques