Multilingual Invoice Translation using Multimodal LLMs

Problem statement Same as before, with focus on leveraging LLM’s vision and language capabilities

Approach

1. Visual Processing with LLM:
   • Utilization of multimodal LLMs for direct invoice image understanding
   • Processing of various invoice layouts and formats through vision capabilities
   • Direct interpretation of mixed-language content including text, numbers, and tables
2. LLM-based Translation System:
   • End-to-end translation using single LLM model:
     • Direct visual understanding of source invoice
     • Language detection and translation
     • Context-aware interpretation of financial terms
     • Preservation of numerical data and formatting
   • Prompt engineering for accurate translations
   • Few-shot learning with examples of correctly translated invoices
3. Structured Output Generation:
   • LLM-guided extraction of translated content
   • Template-based PDF generation using standardized format
   • Automated quality checks through LLM verification

Observation

• Multimodal LLMs show strong capability in understanding various invoice layouts
• Single model handling both vision and translation reduces pipeline complexity
• Need for careful prompt design to ensure translation accuracy
• LLMs maintain context better than traditional translation systems
• Critical financial data preservation is more reliable with LLM understanding

Optimization steps and Results

1. LLM Performance Enhancement:
   • Optimization of prompts for different languages
   • Development of language-specific examples for few-shot learning
   • Implementation of validation checks for numerical data
   • Fine-tuning of vision-language processing for invoices
2. Translation Quality:
   • Creation of specialized prompts for financial terminology
   • Implementation of verification steps for critical information
   • Cross-validation of translated content
   • Confidence scoring for translations
3. Output Generation:
   • Standardized formatting instructions for LLM
   • Quality assurance through LLM verification
   • Automated error detection and correction
   • PDF generation with consistent formatting

Contribution

1. Technical Innovation:
   • Novel application of multimodal LLMs for invoice processing
   • Integration of vision and translation capabilities
   • Advanced prompt engineering for financial document translation
2. Business Impact:
   • Simplified pipeline using single model approach
   • Higher accuracy in context-aware translations
   • Better handling of complex layouts
   • Reduced processing time and human intervention
3. Process Improvement:
   • Elimination of multiple tool dependencies
   • More reliable preservation of critical data
   • Better handling of varying invoice formats
   • Improved accuracy in financial term translation