Architectural Structure of Large Language Models

Large Language Models (LLMs) are artificial intelligence systems built on multi-layered deep neural networks and containing parameters ranging from millions to billions. Their primary objective is to provide general-purpose language understanding and generation capabilities by statistically learning the structural and semantic properties of human language, enabling high accuracy in various natural language processing (NLP) tasks.

The concept of a language model refers to algorithms focused on predicting the next word or sequence of words in a text sequence. Large language models have evolved from this concept by combining it with massive datasets and deep learning architectures to produce more complex, versatile, and context-sensitive solutions. This transformation has been made possible through increases in the volume of data used, the depth of the architecture, the growth in the number of parameters, and advancements in learning processes.

The foundational architecture of these models is based on the transformer structure, first introduced in 2017. The transformer architecture stands out through attention mechanisms that enable parallel processing and efficient modeling of long-range contextual relationships. This architecture typically includes an input layer, multiple encoder and/or decoder blocks, multi-head attention layers, and output layers. Each layer elevates the representation of the input to a more abstract level, allowing the model to understand the internal structure of language. Thanks to this structure, the model can learn complex textual patterns, maintain semantic context, and generate consistent outputs.

However, architectural advancement is not limited to technical modules alone. The performance of large language models also depends on multidimensional factors such as scalability, hardware requirements, computational costs, energy consumption, and ethical responsibilities. As the number of parameters increases, the model’s contextual awareness and expressive capacity improve, but this also brings higher training costs and significant societal impacts. For example, training large models requires computational time equivalent to thousands of GPU days and substantial energy consumption.

Today, models such as GPT-3, GPT-4, BERT, PaLM, and LLaMA exemplify the evolution and application diversity of LLM architectures. While each shares similar structural foundations, they differ in training strategies, scaling principles, usage purposes, and task-specific optimizations. For instance, BERT delivers strong results in semantic inference through bidirectional context modeling, while the GPT series excels in creative text generation due to its unidirectional prediction-based structure.

The architecture of large language models is providing innovative solutions across numerous sectors beyond artificial intelligence research. In fields such as medicine, law, education, finance, and media, these models are used for functions including text generation, classification, summarization, question-answering systems, information extraction, and recommendation systems.

The architectural evolution of large language models is directly linked to the historical development of natural language processing (NLP). This evolutionary process has been shaped by technological progress, increased computational power, and the integration of artificial intelligence approaches that enable deeper modeling of linguistic complexity.

Early applications began in the 1950s with rule-based machine translation and simple text processing systems. These systems operated on fixed rules and lacked flexibility and learning capability. Capturing the structural diversity of language during this period was highly limited.

In the 1960s and 1970s, Markov chains and statistical modeling methods came to the forefront. One of the most common techniques of this era, n-gram models, attempted to represent the probabilistic structure of language by basing the likelihood of a word on the previous n−1 words. However, this approach could only handle short contexts and suffered from serious limitations in capturing long-range dependencies.

In the 1980s, artificial neural networks began to be used in natural language modeling. Multi-layer perceptrons (MLPs) developed during this period offered greater learning flexibility, but failed to achieve desired success due to insufficient hardware and data. Nevertheless, this period marked a crucial transition phase in laying the foundations of data-driven learning paradigms.

By the 1990s, Recurrent Neural Network (RNN) architectures designed to work with sequential data were developed. RNNs enabled modeling of temporal dependencies by storing information from previous inputs in a memory state. However, problems such as vanishing and exploding gradients revealed the limitations of this architecture. These shortcomings were largely addressed by the development of architectures such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), which proved particularly successful in modeling temporal dependencies in language.

In the 2000s, a synthesis emerged between statistical methods and neural network-based approaches. Word embedding techniques transformed words into representations in vector spaces that reflected their semantic relationships. Models such as Word2Vec and GloVe were able to process word similarities and contextual information more deeply. However, the need for sequential data processing and insufficient support for parallel computation limited the scalability of these models.

A true turning point occurred in 2017 with the publication of the paper "Attention Is All You Need"^【1】 by Vaswani and colleagues. This work introduced the transformer architecture, which relies solely on attention mechanisms and can process sequential data in parallel. The transformer created a revolutionary paradigm shift in NLP due to its success in capturing long-range dependencies and its support for parallel computation. This architecture became the foundation for subsequent large language models such as GPT (OpenAI), BERT and T5 (Google), PaLM, LLaMA, and others.

The increase in parameter counts from millions to hundreds of billions has not only heightened computational complexity but also dramatically enhanced the models’ capacity to learn the contextual, semantic, grammatical, and pragmatic aspects of language. Thanks to these developments, large language models are now effective not only in language modeling tasks but also in complex tasks such as logical reasoning, multi-step inference, and multilingual translation.

Today, large language model architectures are no longer confined to natural language processing alone. Transformer-based structures are increasingly prevalent in disciplines such as image processing, speech processing, and multimodal AI systems. These multimodal systems open the door to next-generation intelligent applications by processing different types of data within a unified architectural framework.

The next phase of architectural development will encompass a broader evaluation framework that includes not only model size or performance but also energy efficiency, computational sustainability, explainability, and ethical responsibilities.

The transformer architecture, a revolutionary structure in natural language processing, was first introduced in 2017 in the paper titled "Attention Is All You Need" by Vaswani and colleagues. This architecture offers a more efficient, scalable, and parallelizable structure compared to classical sequential modeling approaches such as RNNs and LSTMs. Its most fundamental difference lies in its ability to process the entire input sequence simultaneously rather than relying on sequential computation. This feature enables more accurate and faster modeling of contextual relationships, especially in long texts.

The transformer architecture is a modular structure based on two main components: encoder and decoder blocks. The encoder block processes the text input and transforms it into high-dimensional, contextual representations. These representations are then used by the decoder block to generate the target output. In tasks requiring sequential input-output relationships such as machine translation, the combined use of these two blocks enhances the model’s ability to understand contextual information and produce appropriate outputs. However, in modern large language models, typically only one of these block types is used. For instance, BERT focuses solely on encoder components for contextual text analysis, while GPT uses only decoder blocks for text generation. On the other hand, hybrid architectures like T5 combine both encoder and decoder blocks to handle diverse language tasks. Each encoder or decoder block consists of fundamental subcomponents including multi-head attention mechanisms, feed-forward neural networks, layer normalization, and residual connections. The synergistic interaction of these components enables the model to effectively resolve both local patterns at the word level and global relationships at the sentence and paragraph level. This architectural structure is a fundamental element determining the strong contextual understanding and high expressive capacity of transformer-based models.

^{General structure of the transformer architecture (generated by artificial intelligence.)}

The most distinctive component of the transformer architecture is the attention mechanism. Specifically known as self-attention or scaled dot-product attention, this approach calculates the relationship between each element in an input sequence and all other elements. This allows the model to effectively learn meaning relationships, word contexts, and structural patterns within the text.

Multi-head attention allows this attention mechanism to operate in parallel across multiple heads. Each head focuses on different types of relationships within the input sequence, producing independent contextual representations. The outputs of these heads are combined to yield a richer and more comprehensive representation. This structure supports the model’s ability to perform multi-layered abstraction.

Unlike traditional sequential models, the transformer architecture does not inherently preserve word order. Therefore, positional encoding is added to each element in the input to enable the model to retain sequential information. These encodings are vectors that represent the position of words within a sentence.

Typically calculated using sine and cosine functions, these vectors provide a fixed-size and continuous structure. This allows the model to correctly learn grammatical structures and meaning relationships by taking into account the position of each word in the sequence.

The success of large language models depends not only on architectural design but also on the depth of the layered neural networks within them, the number of parameters, how these parameters are optimized, and how the training processes are planned. Modern natural language processing systems are increasingly built using artificial neural networks that are larger, deeper, and more complex.

Large language models are generally designed as layered neural networks and characterized by deep learning architectures consisting of numerous hidden layers. Each layer transforms the input into a more abstract and contextual representation, enabling the model to generate meaningful outputs. In transformer-based models, each layer includes subcomponents such as multi-head attention mechanisms, feed-forward networks, layer normalization, and residual connections. Through the synergistic interaction of these components, the model can learn both short-term and long-range contextual relationships. In terms of depth, these models can contain dozens or even hundreds of layers; for example, the GPT-3 model has 96 layers. Within each layer, weights regulate the relationship between input and output and enable the model’s learning process. These weights constitute the parameters the model must learn. The number of parameters is one of the primary determinants of a model’s learning capacity and directly affects its ability to interpret complex patterns in language. In modern large language models, this number reaches the billions. For instance, GPT-3 has approximately 175 billion parameters, PaLM-2 has about 340 billion, and LLaMA-2 models are available in various scales with 7 billion, 13 billion, and 65 billion parameters respectively. However, a high parameter count alone does not guarantee better performance; efficient training processes, regularization techniques, high-quality datasets, and accuracy in application context play roles as decisive as architectural complexity.

An increase in the number of parameters in large language models enhances learning capacity but also raises the risk of overfitting. In overfitting, the model adapts excessively to the training data and loses its ability to generalize to previously unseen data. To prevent this adverse condition, various regularization methods have been developed. One of the most commonly used techniques, dropout, prevents the model from becoming overly dependent on specific structures by randomly deactivating certain neurons. Early stopping halts the training process when validation loss ceases to improve beyond a certain threshold, thereby preventing overfitting. Additionally, data augmentation strategies increase the model’s generalization ability by diversifying the input data. Weight decay penalizes large weights in the model, limiting the learning of overly complex structures. The optimization process also plays a critical role in model success. Algorithms such as Stochastic Gradient Descent (SGD) and Adam (Adaptive Moment Estimation) are frequently used, while more advanced variants such as AdaGrad, RMSProp, and LAMB are employed in larger and distributed systems. Moreover, the correct selection of hyperparameters such as learning rate, batch size, and momentum significantly influences both the stability of the training process and the model’s final performance. In this context, in large language models, not only the architectural structure but also optimization techniques and regularization strategies are critical components shaping overall performance.

The fundamental approach in training large language models is the unsupervised learning paradigm. In this approach, models undergo a pre-training phase on large amounts of unlabeled text data. During this process, the model statistically learns the structural properties, semantic relationships, and contextual patterns of language. Two common pre-training strategies stand out: Masked Language Modeling (MLM) and Causal Language Modeling. In the MLM approach, as in the BERT model, certain words in the input sentence are masked with a special token, and the model is expected to predict these masked words. This enables the model to learn bidirectional contextual relationships. In contrast, autoregressive models like the GPT series predict the next word based solely on previous words, thereby developing the model’s ability to generate sequential text.

After the pre-training phase, a fine-tuning process is performed to enable the model to perform effectively on specific tasks. This process typically uses smaller, task-specific labeled datasets. Adaptation of the model for specific applications such as classification, summarization, sentiment analysis, or question-answering systems occurs during this stage. However, due to the increasing generalization capacity of pre-trained large language models, techniques such as prompt engineering, few-shot learning, and zero-shot learning have become widespread in conjunction with transfer learning. These techniques enable models to produce effective results on new tasks with minimal or no additional training. Thus, large language models have become versatile tools capable of easily adapting to a wide range of natural language processing tasks through a single architecture.

In the success of large language models, not only the architectural design and training methods but also the quality and cleanliness of the training data play a decisive role. These models are typically trained on millions of documents sourced from diverse origins such as Wikipedia articles, digital book archives, news websites, forums, and large-scale web crawls. However, the presence of toxic, misleading, sexist, racist, or ethically inappropriate content within this vast dataset can cause the model to learn and generate harmful biases and stereotypes. This poses serious risks from both security and social responsibility perspectives. Therefore, data cleaning procedures during the preprocessing of training data are of great importance. These procedures include removing inappropriate content from texts, filtering out low-quality or irrelevant documents, and detecting harmful linguistic patterns. Furthermore, filtering algorithms and AI-assisted monitoring tools have been developed to make this process more systematic and automated. In some model development processes, independent ethical review boards have been integrated into the workflow to ensure ethical evaluations. The goal is to achieve outputs that are more aligned with principles of neutrality, reliability, and social responsibility.

Today, training large language models is no longer feasible with single hardware systems due to increasing parameter counts and data volumes. Training such models is only possible through high-capacity distributed systems and parallel processing architectures. In particular, training models with billions of parameters is carried out in high-performance data centers equipped with thousands of GPU or TPU cores. Major distributed training techniques include model parallelism (dividing the model’s layers and components across various devices), data parallelism (training the same model on different data partitions in parallel), and pipeline parallelism. Hybrid approaches combine these techniques to distribute the computational load evenly. However, training such large-scale systems entails significant consequences not only in terms of time and hardware but also in cost, energy consumption, and environmental impact. For example, training models like GPT-3 required weeks of processing time and infrastructure costs amounting to millions of dollars. This situation has also triggered ethical and economic debates regarding the carbon footprint, energy efficiency, equitable access, and sustainability of large language model development.

Large language models (LLMs) are primarily built on the transformer architecture but offer specialized solutions for various natural language processing tasks due to their architectural differences. These models may contain layers based on encoder, decoder, or a combination of both. For instance, the GPT series uses only transformer decoder blocks and is trained using autoregressive (causal) language modeling. This architecture, which focuses on predicting the next word based solely on previous words, makes the model highly effective in tasks such as text generation, question-answering systems, summarization, dialogue, and even code generation. However, it may be limited in tasks requiring bidirectional context analysis. Models like GPT-3 contain 175 billion parameters, while GPT-4 is an advanced model with a multimodal structure capable of processing visual inputs.

In contrast, BERT has an architecture that uses only encoder blocks and can analyze context bidirectionally. It leverages training strategies such as masked language modeling (MLM) and next sentence prediction (NSP). This structure achieves high success in meaning-based tasks such as text classification, sentiment analysis, relationship extraction, and named entity recognition (NER). Variants of BERT include RoBERTa, DistilBERT, ALBERT, and BERTweet. However, BERT is not suitable for direct text generation.

^{General pre-training and fine-tuning procedures for BERT (}Jacob Devlin et al.⁾

The T5 (Text-to-Text Transfer Transformer) model consists of a combination of encoder and decoder blocks and reformulates all NLP tasks as “input text → output text.” This approach allows tasks such as classification, translation, summarization, and question-answering to be solved under a single framework. Variants of T5 such as mT5 (multilingual) and ByT5 (byte-level) extend the model’s applicability to broader domains. The T5 architecture draws attention for its high generalizability across tasks.

^{T5 model architecture (}Yashi Qin⁾

PaLM (Pathways Language Model), one of the larger and multi-task architectures, has hundreds of billions of parameters (e.g., PaLM: 540 billion parameters) and stands out for its multi-task and multilingual capabilities. Similarly, LLaMA models aim for broad accessibility through their open-source nature and low hardware requirements, offering significant advantages for the research community.

Mixture of Experts (MoE) architectures are based on the idea that not all parameters of a model need to be used for every task. This structure activates only specific subcomponents (experts) optimized for particular tasks. As a result, computational cost is reduced and energy efficiency is increased. Important examples in this area include Switch Transformer, GShard, and M6-MoE.

Finally, multimodal models aim to process not only text but also visual, auditory, and other modalities within a single AI framework. These models are used in complex tasks such as text-to-image generation, generating textual responses to visual inputs (e.g., visual question-answering), and interacting with voice commands. Models such as CLIP, Flamingo, GPT-4 (visual+text), Gemini, and Kosmos-1 are leading examples in this field. These architectures are regarded as significant steps toward artificial general intelligence (AGI) due to their human-like multimodal perception and generation capabilities.