Domain-Specific Pretraining of Language Models: A Comparative Study in the Medical Field

TL;DR

This paper compares domain-specific pretraining of language models to general models in the medical field, highlighting efficiency and performance benefits for specialized tasks.

Contribution

It provides a comparative analysis of domain-specific versus general-purpose language models in medical applications, emphasizing the advantages of targeted pretraining.

Findings

Domain-specific models outperform general models on medical benchmarks.

Pretraining on medical data improves model efficiency and accuracy.

Specialized models are more suitable for sensitive medical data handling.

Abstract

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.