Garbage in, garbage out — and in industrial plants, garbage can cost more than a bad quarterly report.
Researchers have published a technique called Measurement Credibility Correction that uses a large language model to screen sensor readings before they reach a forecasting model. The system works by converting descriptions from process documents — manuals, specs, engineering notes — into semantic labels that can be compared against live measurements. When a reading looks plausible on its face but conflicts with what physically should be happening upstream, MCC flags it and substitutes a corrected value. The method adds between 500 and 2,000 parameters on top of whatever prediction backbone is already in place, and the researchers clock its inference overhead at under 0.1 milliseconds per step.
The numbers are striking: across real industrial forecasting and soft-sensing tasks, models equipped with MCC cut mean absolute error by an average of 30.7 percent on real test data and 80.3 percent on datasets with deliberately introduced corruption. That matters because industrial AI failures often trace back to input quality, not model architecture — a miscalibrated sensor or a stale derived reading quietly poisons a prediction long before anyone checks the loss curve.
Most existing approaches to this problem lean on numerical correlations, explicit process equations, or fault labels — assumptions that are frequently unavailable or unreliable when multiple instruments share a common source or feed into each other. Using a language model to pull structured meaning out of plain process documents sidesteps that dependency. Whether the approach holds up when process documentation is incomplete, outdated, or written in mixed languages is the next obvious question — and one the paper does not answer.