Domain-Specific Pretraining of Language Models: A Comparative Study in the Medical Field
This addresses the need for locally runnable, privacy-preserving models in sensitive domains, but it is incremental as it builds on existing pretraining approaches.
The paper tackled the problem of efficiently developing specialized language models for sensitive domains like medicine by comparing domain-specific and mixed-domain pretraining to general pretraining, finding that these methods can be more efficient for specialized tasks.
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.