1 Basic concepts

An LLM goes through two main phases(Lifecycle of an LLM): Training and Inference:

  • Pre-training : it learns to predict the next word or token from large amounts of unlabeled text.
  • Fine-tuning : The model is adapted to specific tasks or domains using labeled datasets.
  • Post-training(Alignment) : it learns useful behaviors such as following instructions, tool use, and reasoning.
  • Inference : The trained model responds to user inputs in real time without updating its internal parameters.

Foundational Techniques that Underpin LLM Architecture :

Module / Capability Key Paper Authors Contribution to LLMs
Deep Representation (CNN) ImageNet Classification with Deep Convolutional Neural Networks (2012) Alex Krizhevsky et al. Demonstrated the power of deep learning via CNNs on large-scale data
Residual Connection Deep Residual Learning for Image Recognition (2015) Kaiming He et al. Enabled training of very deep networks; essential in Transformer sublayers
Sequence Modeling (RNN) Sequence to Sequence Learning with Neural Networks (2014) Ilya Sutskever et al. Introduced encoder-decoder architecture for NLP tasks
Attention Mechanism Attention Is All You Need (2017) Vaswani et al. Introduced the Transformer, replacing recurrence with self-attention
Normalization Layer Normalization (2016) Jimmy Ba et al. Improved training stability in sequence models
Language Representation BERT: Pre-training of Deep Bidirectional Transformers (2018) Devlin et al. Enabled bidirectional contextual language understanding
Generative Pretraining Improving Language Understanding by Generative Pre-Training (2018) Radford et al. (OpenAI) Introduced GPT framework for generative language modeling
Scaling Laws Scaling Laws for Neural Language Models (2020) Jared Kaplan et al. (OpenAI) Quantified model performance growth with scale in parameters and data

1.0 Initial input processing

Concept Description Problem Solved
Tokenization Breaks text into tokens such as subwords, characters, or word pieces Converts natural language into discrete units understandable by models
Vocabulary A predefined set of tokens Reduces Out-Of-Vocabulary (OOV) issues and standardizes encoding

1.1 Vector Representation (Embedding Space)

Component Description Problem Solved
Token Embedding Lookup Each token is mapped to a high-dimensional vector (e.g., 768 or 1536 dimensions) Captures basic semantic relationships between tokens
Sentence/Paragraph Pooling Techniques like Mean Pooling, Attention Pooling Aggregates token-level vectors into sentence-level representation

Multilingual Embedding Alignment is a capability that must be actively built during the training process through the use of specific data, model architectures, and objective functions—it is not something the model acquires naturally on its own. Its implementation relies on training strategies such as shared tokenizers, multilingual corpora, contrastive learning, and parallel sentence alignment mechanisms.

1.2 Contextual Modeling and Semantic Understanding (Transformer Backbone)

Component Description Problem Solved
Multi-layer Transformer Encoder Multi-head self-attention mechanism to encode context Understand long-range dependencies, capture syntax and semantics
Feed-Forward Network (FFN) Enhances non-linear representation ability Improves diversity and depth of model representations
Residual Connection & LayerNorm Stabilizes training, alleviates gradient vanishing Improves trainability of deep networks
transformer

1.3 Output Decoding / Generation

Component Description Problem Solved
Linear Projection + Softmax Maps the Transformer output to a probability distribution over the vocabulary Enables prediction and generation of the next token
Greedy / Beam / Sampling Decoding Strategies to select the next word/token from the probability distribution Controls the quality, diversity, and coherence of generated text

LLM Solution from simple to complect