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

arXiv: https://arxiv.org/abs/2407.14076 · HTML: https://mlsanity.com/paper/2407.14076/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 22 references that are in the map.
- First posted on arXiv 19 Jul 2024.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering](https://mlsanity.com/paper/2203.14371/) (2022)
- [The Stack: 3 TB of permissively licensed source code](https://mlsanity.com/paper/2211.15533/) (2022)
- [MedDialog: Two Large-scale Medical Dialogue Datasets](https://mlsanity.com/paper/2004.03329/) (2020)
- [BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text](https://mlsanity.com/paper/2403.18421/) (2024)
- [Apollo: A Lightweight Multilingual Medical LLM towards Democratizing Medical AI to 6B People](https://mlsanity.com/paper/2403.03640/) (2024)
- [A Continued Pretrained LLM Approach for Automatic Medical Note Generation](https://mlsanity.com/paper/2403.09057/) (2024)
- [MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining](https://mlsanity.com/paper/2012.13978/) (2020)

## Built on by (most cited first, 5 of 5)

- [Task-Adaptive Pretrained Language Models via Clustered-Importance Sampling](https://mlsanity.com/paper/2410.03735/) (2024)
- [Optimizing Retrieval Strategies for Financial Question Answering Documents in Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2503.15191/) (2025)
- [Benchmarking Chinese Medical LLMs: A Medbench-based Analysis of Performance Gaps and Hierarchical Optimization Strategies](https://mlsanity.com/paper/2503.07306/) (2025)
- [From Amateur to Master: Infusing Knowledge into LLMs via Automated Curriculum Learning](https://mlsanity.com/paper/2510.26336/) (2025)
- [NeuroLit Navigator: A Neurosymbolic Approach to Scholarly Article Searches for Systematic Reviews](https://mlsanity.com/paper/2503.00278/) (2025)

## Abstract (arXiv)

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.