Why Language Models Should Not Be Trusted to Extract Numbers From Documents

A technical analysis warns that language models pose a hidden risk when used to extract numerical data from documents, as numeric errors — unlike missing fields — can go undetected for months. Unlike an empty field, which prompts human review, a plausible but incorrect number such as 340,000 in place of 349,000 gets forwarded, quoted, and copied without scrutiny. Research indicates that language models process multi-digit numbers digit by digit, making errors that are close in string format but vastly different in actual value. Structured output schemas, including those offered by providers like OpenAI, guarantee field format compliance but explicitly do not guarantee that the extracted value matches the source document. The author argues that treating a model as a reliable document reader is an unverified assumption, and that a system leaving some fields empty while being accurate on the rest is more trustworthy than one that fills all fields with occasional silent errors.