Recent advances in natural language processing (NLP) can be largely
attributed to the advent of pre-trained language models such as BERT and
RoBERTa. While these models demonstrate remarkable performance on general
datasets, they can struggle in specialized domains such as medicine, where
unique domain-specific terminologies, domain-specific abbreviations, and
varying document structures are common. This paper explores strategies for
adapting these models to domain-specific requirements, primarily through
continuous pre-training on domain-specific data. We pre-trained several German
medical language models on 2.4B tokens derived from translated public English
medical data and 3B tokens of German clinical data. The resulting models were
evaluated on various German downstream tasks, including named entity
recognition (NER), multi-label classification, and extractive question
answering. Our results suggest that models augmented by clinical and
translation-based pre-training typically outperform general domain models in
medical contexts. We conclude that continuous pre-training has demonstrated the
ability to match or even exceed the performance of clinical models trained from
scratch. Furthermore, pre-training on clinical data or leveraging translated
texts have proven to be reliable methods for domain adaptation in medical NLP
tasks.

本文探讨了如何通过在特定领域数据上进行连续预训练，来适应领域特定需求，以提高医学自然语言处理任务的性能。实验证明，通过在临床数据或翻译文本上进行预训练已被证明是在医学领域适应性优化的可靠方法。

针对临床和生物医学文本理解的德语语言模型的综合研究

Comprehensive Study on German Language Models for Clinical and  Biomedical Text Understanding

Large language models (LLMs) have demonstrated a powerful ability to answer
various queries as a general-purpose assistant. The continuous multi-modal
large language models (MLLM) empower LLMs with the ability to perceive visual
signals. The launch of GPT-4 (Generative Pre-trained Transformers) has
generated significant interest in the research communities. GPT-4V(ison) has
demonstrated significant power in both academia and industry fields, as a focal
point in a new artificial intelligence generation. Though significant success
was achieved by GPT-4V, exploring MLLMs in domain-specific analysis (e.g.,
marine analysis) that required domain-specific knowledge and expertise has
gained less attention. In this study, we carry out the preliminary and
comprehensive case study of utilizing GPT-4V for marine analysis. This report
conducts a systematic evaluation of existing GPT-4V, assessing the performance
of GPT-4V on marine research and also setting a new standard for future
developments in MLLMs. The experimental results of GPT-4V show that the
responses generated by GPT-4V are still far away from satisfying the
domain-specific requirements of the marine professions. All images and prompts
used in this study will be available at
this https URL

该研究基于 GPT-4V 进行了海洋分析的初步和综合性案例研究，评估了 GPT-4V 在海洋研究中的性能，并为未来 MLLM 的发展设定了新的标准。实验结果表明，GPT-4V 生成的回应仍远未满足海洋领域的特定需求。