The Data Exchange with Ben Lorica Automating Unstructured Data Extraction with LLMs
Aug 8, 2024
Shuveb Hussain, co-founder of Unstract, discusses his innovative no-code platform that automates the extraction of structured data from unstructured documents. He highlights the rise of prompt engineers and their role in data transformation. The conversation dives into the complexities of using large language models and the critical importance of quality optical character recognition. Hussain also addresses the fine-tuning of language models for specific needs and the integration of diverse document types, showcasing how these advancements enhance data processing efficiency.
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Unstruct's Origin
- Unstruct was inspired by the realization that large language models (LLMs) can reason and follow instructions.
- This allows them to structure unstructured data, a common problem.
Unstruct's Purpose
- Use Unstruct to extract structured data from unstructured documents like PDFs.
- Output the data as JSON or SQL for use in data warehouses or databases.
Ensuring Accuracy with LLM Challenge
- Unstruct uses "LLM challenge," a technology that employs two LLMs from different vendors.
- This helps ensure accuracy by requiring consensus between the models and setting non-consensus values to null.