The abstract understanding of natural languages helps LLPs infer the probability of words from different contexts, which it then uses across a wide variety of tasks, one of which is described below:
Lemmatization or stemming – Every word in our spoken languages arises out of a ‘root word,’ which is then altered and given different meanings based on where it is used.
For example, ‘Dict’ is a Latin word meaning ‘to say,’ which, when prefixed or suffixed with other alphabets, can mean several different things used in various contexts. While ‘predict’ is about ‘foreshadowing’ an incident, ‘dictation’ is a ‘spelling or writing’ activity.
Similarly, lemmatization entails reducing a word to its most basic form, which the model can learn and memorize. The algorithm identifies this ‘Part-of-speech Tagging (POS-tagging) to find similar root words across other textual references.
With several applicability like the above, the use cases of such language models are vast and can be applied to text, voice content, handwriting, and more.
However, as AI is evolving, so to the complexity and scale of such Language Models, today they don’t just do part-of-speech (POS) tagging or machine translation but can also go beyond words into contextual pattern detections. Their scale is defined by the datasets of texts they can consume, and Large Language Models (LLMs) can be trained on millions of data sets.