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.
@article{arxiv.2407.14076,
title = {Domain-Specific Pretraining of Language Models: A Comparative Study in the Medical Field},
author = {Tobias Kerner},
journal= {arXiv preprint arXiv:2407.14076},
year = {2024}
}