Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
Data Science
Towards Data Science·15d ago·5 min read
Loop Engineering with Adaptive Parsing in Action: Parsing Flat Tables with Azure and Figures with a Vision LLM
TL;DR
Parsing complex documents can be challenging, especially with flat tables and figures. This work demonstrates the use of Azure for parsing flat tables and a vision model for interpreting figures.
✦ Why It Matters
Engineers can implement Azure's parsing capabilities today to automate the extraction of data from flat tables in documents.
Key Takeaways
- Start with a cheap parser to minimize costs.
- Use a feedback loop to determine when to escalate parsing.
- Integrate vision LLMs for complex document structures.
- Ensure auditability by tracking parsing decisions and outputs.
How It Works
The adaptive parsing framework begins with a low-cost parser, evaluating its output through a series of checks. If the output is deemed insufficient, the system escalates to a more advanced parser, such as Azure Layout or a vision LLM, based on the specific needs of the document structure.
This cascading approach allows for efficient resource allocation while maintaining high accuracy in document parsing.