A large language model learns dependencies. Precisely it learns to understand and capture relationships between different elements of language — words, phrases or sentences. These relationships also include syntactic dependencies (subject-verb agreement) and semantic dependencies (‘tiger’ and ‘animal’ are related concepts).

When there is a sentence with a missing word, the model is able to predict that word based on the context and dependencies it has learnt from the vast amount of text it has been trained on. It also understands the meaning of a sentence and generates coherent responses based on this understanding.

The model’s learning of dependencies is attributed to pattern recognition in the text it is trained on, capturing statistical regularities and adjusting its internal parameters accordingly while being trained.

It is thus ready with the complex structure of the language and performs various NLP tasks.