Part 1: Large Language Models – Definitions, Process, and Applicability

Technology is changing life as we see it right before our eyes. It can be daunting to make sense of. It frees us how we are helped performing tasks, often taking hours and days of strenuous manual labor. Take, for example, the claims identifying and segmenting process; a claims analyst would earlier take long hours sifting through documents, making notes, finding the history of claimants and previous BI claims, segregating and sorting to pass them to the claims manager while many times inadvertently missing essential messages, or unknowingly ignoring previous car damage which was not claimed then. Today with the introduction of technology such as, the engine assists claims analysts with these tasks, being their supervisor, lending insights and finding clues, and picking patterns in unstructured data that can help make quick decisions without anything being missed. This ensures that the process isn’t just complete but is more accurate and on point and benefits insurers by saving costs, managing high severity, and bringing down litigation.

To understand how AI engines like Charlee work is to configure the nuts and bolts that go into making the technology. Understanding the basic frameworks and structures helps envision where these technologies can best be deployed to benefit insurers the most in their critical tasks.

Language is the basis of human communication, and unsurprisingly language is the basis of technologies like artificial intelligence and machine learning. Technology must be trained on methods that closely resemble or mimic human touch to recognize behavior patterns or understand textual references.

Called Language Models (LMs), this programming language in deep learning mainly extracts contextually relevant information from textual data. Language Models are, therefore, the basis for developing applications that can distill valuable insights from raw data and texts.


In its basic form, language models use machine learning (ML) to assess the probability distribution of words per word sequences to predict the most likely talk to appear in a sentence based on a past entry. By learning from past textual data, such language models can;

  • Produce original text
  • Predict the following word in the text string
  • Recognize speech
  • Optical character recognition
  • Recognize handwriting

Language models work on the grammar syntax instead of the way people write and the predictable patterns in such behavior, which the model learns and applies.


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.

Written by: Dr. Charmaine Kenita

Technology and Business Writer


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