Knowledge cutoff

In machine learning, a knowledge cutoff (or data cutoff) is the point in time beyond which a large language model has not been trained on new data. Since large language models are pretrained, any model's knowledge is fixed at what it was trained on before deployment; information about events after this date is absent from the model's training data. The model cannot access information about later events without a system for real-time data access such as retrieval-augmented generation, which fetches new information from an external database. Knowledge cutoffs can introduce limitations like hallucinations, where the model generates confident (but false) statements, information gaps, and reduced accuracy on evolving knowledge. Research has shown that knowledge cutoffs have safety-critical implications in domains such as healthcare, where outdated knowledge can lead to harmful recommendations. In the clinical domain, models with a later knowledge cutoff had greater accuracy on questions reflecting newer guidelines.