The Versatility of Large Language Models: A Comprehensive Review and Structured Survey of Architectures, Applications, Challenges, and Future Trajectories
Aug 2026· Archives of Computational Methods in Engineering· 0 citations· 184 references
TL;DR
This survey reviews the evolution of language models from early statistical approaches to modern Transformer-based architectures and summarizes key developments, including attention mechanisms, scaling laws, alignment techniques, and efficient inference methods.
Abstract
Large Language Models (LLMs) have emerged as a transformative technology in artificial intelligence, significantly advancing natural language understanding, generation, and reasoning capabilities. This survey reviews the evolution of language models from early statistical approaches to modern Transformer-based architectures and summarizes key developments, including attention mechanisms, scaling laws, alignment techniques, and efficient inference methods. The paper further explores the growing impact of LLMs on everyday life and a wide range of application domains, including healthcare, finance, education, agriculture, marketing, software engineering, and scientific research. To provide a systematic perspective, LLM applications are categorized according to their maturity level and integration across major artificial intelligence subfields, such as natural language processing, multimodal learning, intelligent decision support, autonomous agents, and knowledge-based systems. The survey highlights how these models enhance automation, data-driven decision-making, personalized services, and human–AI interaction across both consumer and industrial environments. Despite their remarkable capabilities, LLMs face several critical challenges, including high computational costs, limited interpretability, hallucinations, privacy and security risks, ethical concerns, and environmental sustainability issues. Existing mitigation approaches and recent advancements are reviewed to assess their effectiveness and limitations. Finally, the paper outlines key future research directions, including trustworthy and explainable AI, efficient model architectures, domain-specific adaptation, multimodal intelligence, and human-centered alignment. This survey provides a comprehensive overview of the current landscape, challenges, and future prospects of LLMs, serving as a valuable reference for researchers and practitioners.
Large language models (LLMs) are built on the classic Transformer architecture and have become a core driving force for the rapid development of modern artificial intelligence. This paper presents a systematic review of LLMs, elaborating on their fundamental working principles, mainstream open-source models, effective lightweight optimization methods, retrieval-augmented generation frameworks and key human-value-aligned technologies. Nowadays, LLMs have been widely applied in practice. Typical scenarios include intelligent text generation, professional knowledge-based question answering and automated code generation, delivering remarkable value to both industries and academia. However, their large-scale industrial application is still restricted by multiple challenges. The major issues involve content hallucination, poor model interpretability, excessive computing resource consumption, potential ethical risks and unsatisfactory multimodal integration capability. This paper also forecasts the future development directions of LLMs, such as lightweight deployment on edge devices, safety-focused human value alignment, in-depth cross-modal fusion and customized large models for vertical industries. Additionally, it collects a number of representative cases, which can offer solid references and practical guidance for relevant researchers and engineering practitioners to carry out further studies.
Comparison of GPT-4, BERT (bidirectional encoder representations from transformers), Gemini, and DeepSeek large language models (LLM), focusing on architectures, training methodologies, and real-world applications reveals GPT-4 excels in natural language generation and complex reasoning, supporting up to 128K tokens with moderate latency and higher costs making it effective for conversational artificial intelligence (AI).
Kavish Sanghvi, Aparna S. Sharma, Surbhi Hooda· Computer Science and Informa...· 0 citations
Abstract--Natural Language Processing (NLP) has emerged as a major branch of Artificial Intelligence (AI) that allows computers to effectively understand, interpret, and generate human language. The recent advances in machine learning, deep learning and transformer-based architectures have considerably improved the performance of NLP systems on a wide range of applications. This paper presents a comprehensive review of the evolution of NLP from traditional rule-based approaches to modern transformer models including BERT and GPT. It covers the major methodologies including text preprocessing, feature representation, machine learning, deep learning and transformer-based language modelling. Moreover, the study elaborates on the use of NLP in healthcare, education, business, finance, customer service, social media, and intelligent communication and highlights its role in enhancing automation, decision-making, and human–computer interaction. In addition, the paper discusses the major challenges faced by current NLP systems, including language ambiguity, multilingual processing, computational complexity, model bias, privacy, and explainability. Finally, future research directions, including lightweight language models, multilingual NLP, explainable AI, and multimodal intelligence, are presented. The findings demonstrate that NLP continues to transform intelligent systems and is expected to play an increasingly significant role in the development of next-generation AI technologies.
P. Kalaiselvi· International Journal of Eme...· 0 citations
The field of natural language processing (NLP) underwent a sea change with the introduction of large language models (LLMs). The NLP systems have progressed from simple rule-based systems to sophisticated transformer-based generative pre-trained models. These models have been pre-trained on vast amounts of data and encompass tens to hundreds of billions of parameters. They demonstrate excellent emergent abilities, including complex reasoning, instruction following and in-context learning. This paper presents a comprehensive overview of the LLMs with a detailed discussion of one of the most widely used LLM families, namely, the GPT model family. The architecture of the LLMs is explored to understand the nuances of the modern approach to natural language processing. The GPT family is discussed in detail to understand its rapid evolution over a short period. The various application domains of LLMs and the challenges posed by LLMs are enumerated. This paper aims to serve as a foundational resource for researchers and practitioners navigating the rapidly evolving field of large language models.
P. Vinayagam· ICTACT Journal on Soft Compu...· 0 citations
Natural Language Processing (NLP) has become a cornerstone of artificial intelligence, enabling machines to process, understand, and generate human language. With the increasing adoption of machine learning, deep learning, and large-scale transformer models, NLP has made significant progress in the past decade. Modern NLP systems are used in applications ranging from machine translation, sentiment analysis, chatbots, and automated summarization to knowledge extraction and conversational agents. Transformer-based architectures and large language models (LLMs) have drastically improved context understanding, semantic representation, and generation quality. This paper provides a comprehensive survey of NLP, discussing its components, historical evolution, applications, datasets, evaluation metrics, recent advancements, and challenges. A detailed literature review based on studies from 2022–2026 is presented, highlighting the role of transformer models, deep learning architectures, and emerging trends such as multi-lingual NLP, domain-specific models, and ethical considerations. Finally, the paper explores future research directions to address low-resource languages, model fairness, and real-world applicability.
Ankita Vijay Shinde, Sunil Tanaji Salunkhe,, Bharati Bhaskar Khandagale· International Journal of Adv...· 0 citations
The concept of the intelligent agent represents a long‐standing pursuit in artificial intelligence. Recent breakthroughs in large language models (LLMs) have catalyzed a paradigm shift, enabling the development of sophisticated agents that exhibit advanced reasoning, planning, and tool‐use capabilities across diverse domains. These LLM‐based agents, which leverage natural language as a universal interface for cognition and interaction, are rapidly advancing from theoretical constructs to practical applications, ranging from autonomous task assistants to complex multi‐agent simulations of social and economic systems. This paper provides an integrative survey of this burgeoning field. We first establish an organizing framework for understanding LLM‐based agents, systematically deconstructing both single‐agent and multi‐agent systems into their core components. We analyze the architectural principles and key mechanisms that underpin their intelligence, including planning paradigms, memory structures, and reflection‐based self‐improvement. We further investigate the dynamics of multi‐agent systems, exploring coordination strategies, communication protocols, and organizational structures. The paper also covers the crucial aspects of performance evaluation, highlighting influential benchmarks and identifying key challenges. Finally, we synthesize the current landscape to discuss the primary challenges, such as the intrinsic limitations of LLMs and the complexities of ensuring safety and alignment, and chart a course for future research directions, including the drive toward continual learning and enhanced multi‐modal capabilities.
Yuheng Cheng, Ceyao Zhang, Zhengwen Zhang et al.· WIREs Data Mining and Knowle...· 1 citation