All You Should Know About Large Language Models (LLMs)

This article examines recent advancements in Large Language Models (LLMs), focusing on efficiency improvements and practical implementation. Through analysis of literature from 2021 to present, we identify key innovations making these models more accessible. We highlight three main areas of progress...

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Bibliographic Details
Main Authors: Alaktif, Abadila, Chergui, Meriyem, Daoudi, Imane, Ammoumou, Abdelkrim
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Computational efficiency
Computational modeling
Fine tuning
Focusing
Large language models
Reviews
Speech enhancement
Technological innovation
Text processing
Transformer
Transformers
Online Access:Request full text
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Summary:This article examines recent advancements in Large Language Models (LLMs), focusing on efficiency improvements and practical implementation. Through analysis of literature from 2021 to present, we identify key innovations making these models more accessible. We highlight three main areas of progress: enhanced Transformer architectures enabling longer text processing with fewer resources, novel fine-tuning techniques for efficient task adaptation, and expanded applications beyond language processing into vision, speech, and reinforcement learning. The review evaluates efficiency innovations including patternbased approaches and dynamic attention mechanisms, comparing their benefits and trade-offs. Our analysis provides insights for researchers and practitioners working to make LLMs more practical and accessible, particularly addressing deployment challenges like memory and computational constraints.
ISSN:2771-7402
DOI:10.1109/CommNet63022.2024.10793270