  There are many cases where LLMs are used for specific tasks in a single
domain. These usually require less general, but more domain-specific knowledge.
Highly capable, general-purpose state-of-the-art language models like GPT-4 or
Claude-3-opus can often be used for such tasks, but they are very large and
cannot be run locally, even if they were not proprietary. This can be a problem
when working with sensitive data. This paper focuses on domain-specific and
mixed-domain pretraining as potentially more efficient methods than general
pretraining for specialized language models. We will take a look at work
related to domain-specific pretraining, specifically in the medical area, and
compare benchmark results of specialized language models to general-purpose
language models.
