Which type of NLP model uses large datasets for autonomous learning?

Transformer-based models are designed to harness vast amounts of data for autonomous learning, making them highly effective in natural language processing (NLP) tasks. These models utilize mechanisms like self-attention, which allows them to weigh the significance of different words in a sentence based on their context, effectively capturing relationships and meanings within the data.

The training process of transformer-based models involves exposing them to extensive datasets, from which they learn patterns, grammar, vocabulary, and even nuances of language usage without explicit programming for each rule. This allows them to generate text, translate languages, summarize documents, and perform other tasks with a level of fluency that mimics human understanding.

The other options do not similarly leverage large datasets for autonomous learning. Rule-based systems rely on predefined rules and do not adapt or learn from data. Expert systems embed human expertise and typically do not involve vast datasets for learning but rather for reference. Procedural models follow specific sequences of actions or rules and lack the capacity for autonomous learning that characterizes transformer-based models. Thus, the capability of transformer models to learn and improve from large datasets is what sets them apart in the realm of NLP.