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Few limitations of using off-the-shelf pre-trained LLMs:


  • They’re usually trained offline, making the model agnostic to the latest information (for example, a chatbot trained from 2011–2018 has no information about COVID-19).
  • They make predictions by only looking up information stored in its parameters, leading to inferior interpretability.
  • They’re mostly trained on general domain corpora, making them less effective on domain-specific tasks. There are scenarios when you want models to generate text based on specific data rather than generic data. 
  • For example, a health insurance company may want their question answering bot to answer questions using the latest information stored in their enterprise document repository or database, so the answers are accurate and reflect their unique business rules.
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