Large language models (LLMs): survey, technical frameworks, and future challenges. Kumar, P. Artificial Intelligence Review, 57(10):260, August, 2024. ![Large language models (LLMs): survey, technical frameworks, and future challenges [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{kumar2024a,
title = {Large language models ({LLMs}): survey, technical frameworks, and future challenges},
volume = {57},
issn = {1573-7462},
shorttitle = {Large language models ({LLMs})},
url = {https://doi.org/10.1007/s10462-024-10888-y},
doi = {10.1007/s10462-024-10888-y},
abstract = {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.},
language = {en},
number = {10},
urldate = {2024-11-24},
journal = {Artificial Intelligence Review},
author = {Kumar, Pranjal},
month = aug,
year = {2024},
keywords = {Machine learning, Neural networks, Artificial Intelligence, Artificial intelligence, Generative language models, Large language models, Natural language processing},
pages = {260},
}
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