B2B Document Extraction: Rules or LLM, Which to Choose?

B2B Document Extraction: Rule-Based or LLM, Which Approach to Favor?

A practical comparison between rule-based PDF extraction and an approach using advanced language models (LLM) has been conducted within a realistic B2B ordering scenario. Each method has distinct characteristics that influence their effectiveness depending on the context of use.

Rule-Based Extraction

This method relies on the application of specific techniques to identify and extract information from PDF files. In this approach, pytesseract is often used to extract text from documents.

• Advantages:

  • Offers high accuracy when dealing with standardized document formats.
  • Requires fewer resources compared to artificial intelligence models.

• Disadvantages:

  • Struggles to adapt to variations in document formats.
  • Requires manual maintenance and adjustment of rules.

LLM-Based Extraction

The LLM approach utilizes advanced language models to process and extract information from documents. Tools like Ollama and LLaMA 3 are integrated to accomplish this task.

• Advantages:

  • Ability to understand context and adapt to different document formats.
  • Reduces the need for manual configuration.

• Disadvantages:

  • Demands greater computational resources.
  • May generate errors if the model is not properly trained.

Conclusion

Both B2B document extraction methods have their own advantages and disadvantages. The choice between a rule-based approach and an LLM approach depends on the specific needs of the user, particularly in terms of accuracy, flexibility, and available resources.