“The greatest [drawback] That is, they are potential prediction machines, and they will be wrong in ways that are not just ‘these are not wrong words’.
In a conversation with ARS Technica, AI researcher and data journalist Simon Willison identified a number of important concerns about the use of LLM for OCR. “I still think that the biggest challenge is the risk of accidental guidelines,” he says, immediately injecting (accidentally in this case) is always cautious that can eat LLM’s blasphemous or contradictory instructions.
“This is the fact that the table interpretation errors can be disastrous.” “In the past, I had a lot of issues where a vision LLM has faced the wrong line of data with the wrong headline, resulting in the result of absolute rubbish. Also, wherever the text is sometimes invalid, a model can only invent a text.”
These issues are particularly troubled when acting on financial statements, legal documents, or medical records, where a mistake is at risk. The problems of reliability mean that these tools often require human surveillance, and limits their value to fully automatic data extraction.
The way forward
Even in our seemingly advanced age AI, there is still no perfect OCR solution. The race to unlock data from PDFS continues, such as companies Google Offer Generative AI products now aware of the context. Some motivations to open the PDF in AI companies, as Willes have observed, undoubtedly include the potential training data acquisition: “I think Mr. Declaration of Mr is clear evidence that documents – not just PDF – are a huge part of his strategy, which will provide a great deal of training.”
Whether it is analyzed by AI companies to train training data or historical census, as these technologies improve, they can unlock the knowledge of the knowledge trapped in the digital formats, primarily for human use. This can create a new golden period of statistics analysis.