Large language models (LLMs): survey, technical frameworks, and future challenges

被引:11
|
作者
Kumar, Pranjal [1 ]
机构
[1] Lovely Profess Univ, Sch Comp Sci & Engn, Dept Intelligent Syst, Phagwara 144411, Punjab, India
关键词
Generative language models; Artificial intelligence; Natural language processing; Machine learning; Neural networks; Large language models; ARTIFICIAL-INTELLIGENCE; KNOWLEDGE;
D O I
10.1007/s10462-024-10888-y
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Artificial intelligence (AI) has significantly impacted various fields. Large language models (LLMs) like GPT-4, BARD, PaLM, Megatron-Turing NLG, Jurassic-1 Jumbo etc., have contributed to our understanding and application of AI in these domains, along with natural language processing (NLP) techniques. This work provides a comprehensive overview of LLMs in the context of language modeling, word embeddings, and deep learning. It examines the application of LLMs in diverse fields including text generation, vision-language models, personalized learning, biomedicine, and code generation. The paper offers a detailed introduction and background on LLMs, facilitating a clear understanding of their fundamental ideas and concepts. Key language modeling architectures are also discussed, alongside a survey of recent works employing LLM methods for various downstream tasks across different domains. Additionally, it assesses the limitations of current approaches and highlights the need for new methodologies and potential directions for significant advancements in this field.
引用
收藏
页数:51
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