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LLMs Now Documents Can Be Cheaper

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LLMs Now Documents Can Be Cheaper
Photo: Jo Lin / Unsplash

Scand letters, uploaded PDFs and merged inserts often come as page stream and must be divided into separate documents before classification, data extraction or routing processes. To make this process more efficient, researchers developed a new method.

Traditional approaches include limitations:

  • Every page limit decision requires a separate model call, increasing the cost of processing
  • Zero-out large language models (LLMs) are able to detect limits without special training but efficient w Tags
  • Page Classification (PC) and Border Decision (BD) methods can only solve a limit at one time

Multi-Split Boundary Decision (MSBD) The approach was designed to solve this problem. The new formulation is able to detect multiple document limits in a call of LLMs. This significantly reduces both process time and API call cost.

Document fragmentation is a basic step of document management workflows. These types of optimizations are practical value, especially when document is processed in large volume in corporate environments. Less model call allows lower cost and quick automation.

#Document Processing #LLM Optimization #Artificial Intelligence #PDF Segmentation