Large language model training

A large language model (LLM) is a deep neural network that can perform a variety of natural language processing tasks. It can operate because it is trained on huge volumes of data. It is trained using natural language or human language samples. After parsing large volumes of information, the LLM can recreate answers by generating probabilities of a series of words. But an LLM is only as good, or as bad, as the data it is trained on.
With structured and enriched enterprise data, LLMs can provide substantially improved responses with higher accuracy. By using a DITA content format or similar, the LLMs can learn and understand granular units of content and their relationships, helping the LLM extract and process specific components more accurately. When metadata is added to the mix, LLMs can be used to provide personalized answers to users – by taking into consideration their preferences, interests, or contextual information.

Example use cases

  • Parse large user manuals to send accurate answers to user queries
  • Provide the source to the answer your chatbot is generating
  • Deliver hyper-personalized content to users 
  • Answer medical questions in a natural language

Key benefits

  • Easily index big data sets
  • Turbo charge LLM results when trained on structured content repositories
  • Granular information extraction due to DITA componentization
  • Domain-specific accurate, informed, and precise responses