Scholarlink

Background <p>Large language models (LLMs) such as ChatGPT have rapidly revolutionized the way computers can analyze human language and the way we can interact with computers.</p> Objective <p>To give an overview of the emergence and basic principles of computational language models.</p> Methods <p>Narrative literature-based analysis of the history of the emergence of language models, the technical foundations, the training process and the limitations of LLMs.</p> Results <p>Nowadays, LLMs are mostly based on transformer models that can capture context through their attention mechanism. Through a&#xa0;multistage training process with comprehensive pretraining, supervised fine-tuning and alignment with human preferences, LLMs have developed a&#xa0;general understanding of language. This enables them to flexibly analyze texts and produce outputs of high linguistic quality.</p> Conclusion <p>Their technical foundations and training process make large language models versatile general-purpose tools for text processing, with numerous applications in radiology. The main limitation is the tendency to postulate incorrect but plausible-sounding information with high confidence.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Technische Grundlagen großer Sprachmodelle

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月10日

查看全文
  • Christian Blüthgen

摘要

Background

Large language models (LLMs) such as ChatGPT have rapidly revolutionized the way computers can analyze human language and the way we can interact with computers.

Objective

To give an overview of the emergence and basic principles of computational language models.

Methods

Narrative literature-based analysis of the history of the emergence of language models, the technical foundations, the training process and the limitations of LLMs.

Results

Nowadays, LLMs are mostly based on transformer models that can capture context through their attention mechanism. Through a multistage training process with comprehensive pretraining, supervised fine-tuning and alignment with human preferences, LLMs have developed a general understanding of language. This enables them to flexibly analyze texts and produce outputs of high linguistic quality.

Conclusion

Their technical foundations and training process make large language models versatile general-purpose tools for text processing, with numerous applications in radiology. The main limitation is the tendency to postulate incorrect but plausible-sounding information with high confidence.