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“One of the primary concerns with large language models (LLMs) is their nature as probabilistic prediction tools,” notes researcher Willis. “They don’t just misinterpret a word; they can overlook entire sections in lengthy documents where the format is repetitive, something traditional Optical Character Recognition (OCR) systems generally avoid.”
In a discussion with Ars Technica, AI researcher and data journalist Simon Willison highlighted several key issues associated with employing LLMs for OCR tasks. “The most significant challenge remains the danger of unintentional instruction following,” he cautions, emphasizing the risks posed by accidental prompt injections that could lead to harmful or contradictory directives being processed by LLMs.
“Additionally, errors in interpreting tables can have serious repercussions,” Willison mentions. “I’ve encountered numerous instances where a vision LLM mismatches data entries with the wrong labels, creating outputs that may appear correct but are fundamentally flawed. Sometimes, when faced with unreadable text, a model may fabricate content entirely.”
These challenges are of particular concern in critical fields such as finance, law, and medicine, where inaccuracies could have serious consequences, including risks to personal safety. Such reliability issues indicate that these AI tools often require diligent human oversight, thus restricting their usability for fully automated data extraction processes.
Looking Ahead
Despite advances in artificial intelligence, an ideal OCR solution remains elusive. The ongoing effort to extract data from PDF formats continues, with major players like Google developing context-aware generative AI applications. According to Willis, part of the impetus behind unlocking PDF data for AI companies could relate to the acquisition of training data: “Mistral’s recent announcement strongly suggests that handling documents—beyond just PDFs—is central to their strategy, likely because it can serve as additional training data.”
As these technologies evolve, they may enable access to a wealth of information currently confined to digital formats created for human reading. This could usher in a new era of data analysis potential; however, it also carries the risk of persistent errors remaining unnoticed, depending on the reliability of the technology and the degree of trust placed in it.
Source
arstechnica.com