AI Large Model Introduction

1. Introduction to Large AI Models

Overview of AI and Large Models

What is AI (Artificial Intelligence)?

AI, short for Artificial Intelligence, refers to technologies and methods that enable computer systems to simulate human intelligence. The core goal of AI is to make machines perform tasks that normally require human intellect, such as natural‑language understanding, image recognition, decision‑making, and problem solving.

The history of AI dates back to the 1950s and has passed through several phases, including symbolic AI and connectionism. In recent years, breakthroughs in deep learning and the widespread availability of massive computing resources have ushered in a period of rapid growth for the field.

What is a Large Language Model (LLM)?

A Large Language Model (LLM) – also called a “large model” – is a deep‑learning language model whose parameter count reaches billions or even trillions. By pre‑training on massive text corpora, an LLM learns statistical regularities and semantic representations of language, giving it the ability to understand, generate, and manipulate natural language.

The Relationship Between AI and Large Models

AI is the broadest technical domain, encompassing all methods that give machines intelligence. Within this vast field, machine learning is the core branch of AI that learns patterns from data automatically. See the diagram below:

Machine learning itself contains several learning paradigms:

Supervised learning – requires labeled data
Unsupervised learning – discovers patterns from unlabeled data
Reinforcement learning – learns optimal strategies through interaction with an environment

These paradigms represent different methodological approaches that can be used alone or in combination.

Deep learning is a sub‑field of machine learning and sits at its core. It relies on multi‑layer neural networks to learn hierarchical data representations and forms the backbone of modern AI.

Generative AI is an application direction of machine learning that is tightly linked to deep learning. It focuses on creating new content, and large language models are the primary implementation of generative AI for text.

Large language models sit at the heart of deep learning and represent a typical application of deep‑learning techniques to natural‑language processing. Built on the Transformer architecture and trained on massive corpora, they embody the cutting edge of AI today.

Why Large Models Matter

The emergence of large models marks a pivotal shift from narrow, task‑specific AI (Narrow AI) toward more general systems (General AI). Traditional AI models must be designed and trained separately for each task, whereas large models use a pre‑training + fine‑tuning paradigm to achieve “one model, many tasks.”

This paradigm shift brings four key advantages:

Lower development cost – no need to train a new model from scratch for every task.
Higher performance – massive pre‑training endows the model with rich linguistic knowledge.
Better generalization – pre‑trained models transfer more effectively across tasks.
Simplified deployment – a unified architecture eases standardization, deployment, and maintenance.

Core AI Vocabulary

AIGC (AI‑Generated Content)

AIGC stands for AI‑Generated Content and refers to the automatic creation of text, images, audio, video, and other media using AI techniques. Unlike traditional content production, AIGC’s hallmark is that the output is generated by AI models based on learned data distributions rather than simple retrieval, copying, or template filling.

Typical AIGC applications include:

Text generation – article writing, code generation, dialogue creation, etc.
Image generation – artistic creation, style transfer, image editing, etc.
Audio generation – speech synthesis, music composition, etc.
Video generation – video creation and editing.

The technological foundation of AIGC is generative AI models, with large language models serving as the core engine for text‑based AIGC.

Generative AI

Generative AI is an application direction of machine learning that focuses on creating new content rather than merely classifying or predicting. By learning the probability distribution of training data, generative AI can produce novel samples that resemble the original data. Most generative models are built on deep neural‑network architectures.

Machine Learning (ML)

Machine learning is the central branch of AI. Its essence is to let algorithms automatically discover patterns and regularities from data, without relying on explicit programming rules. ML optimizes model parameters so that the model can make accurate predictions or decisions on unseen data.

A typical ML workflow includes:

Data input – providing a training dataset.
Model training – the algorithm learns patterns from the data.
Parameter optimization – adjusting weights via loss functions and optimizers.
Model evaluation – testing performance on a held‑out set.

Plain‑language analogy: Machine learning is like a person who, after seeing many pictures of cats, can instantly recognize a new cat they’ve never seen before. In ML, we “feed” the computer data, it learns the underlying rules, and then it can handle new situations. In short: feed data → let the computer learn patterns → the computer becomes smart enough to predict new things.

Supervised Learning

Supervised learning is a paradigm where the training data includes both input features and corresponding labels (the “correct answers”). The model learns a mapping from inputs to outputs. Data formats include:

Inputs – feature vectors (e.g., text embeddings, image features).
Outputs – labels (e.g., class categories, regression values).

The goal is to learn the function that maps inputs to outputs f: X → Y. Supervised learning requires large amounts of high‑quality labeled data; the quality of the labels directly impacts model performance. It is suited for tasks with clear objectives, such as image classification, text classification, and regression.

Plain‑language analogy: Supervised learning is like a teacher giving students solved problems. Each example comes with a standard answer (“this is a cat,” “that is a dog”). After seeing many labeled examples, the computer learns how to classify new photos, much like memorizing word‑definition pairs to translate new words.

Unsupervised Learning

Unsupervised learning extracts structure and patterns from data that lack explicit labels. Its main goals are to discover hidden regularities, typically through:

Clustering – automatically grouping data into natural categories.
Dimensionality reduction – mapping high‑dimensional data to a lower‑dimensional space while preserving key information.
Density estimation – modeling the probability distribution of the data.

Unsupervised learning does not need labeled data, but because the learning objective is vague, evaluation can be challenging. Self‑supervised learning is a special form of unsupervised learning that creates auxiliary tasks (e.g., predicting the next word, image in‑painting) to learn representations from unlabeled data. The pre‑training stage of large language models is essentially self‑supervised learning.

Plain‑language analogy: Unsupervised learning is like giving a child a pile of LEGO bricks without instructions and letting them discover patterns—sorting by color, grouping similar shapes—without any “right answer.” It’s akin to a baby learning about the world through play.

Reinforcement Learning (RL)

Reinforcement learning teaches an agent to act in an environment by receiving feedback (rewards or penalties). Core components:

State – the current situation of the environment.
Action – a move the agent can make.
Reward – the feedback signal indicating how good the action was.
Policy – the mapping from states to actions.

RL shines in sequential decision‑making problems such as game AI, robot control, and dialogue optimization. ChatGPT’s training incorporated RL with Human Feedback (RLHF) to refine its outputs.

Plain‑language analogy: RL is like training a puppy: give a treat for good behavior, a gentle “no” for mistakes. The computer tries actions, learns from the resulting scores, and gradually discovers the best strategy—just as AlphaGo improved by winning and losing games.

Deep Learning

Deep learning is a sub‑field of machine learning that builds multi‑layer, non‑linear models using artificial neural networks (ANNs). “Depth” refers to the presence of many hidden layers, enabling hierarchical feature learning:

Shallow layers – capture low‑level features (edges, textures).
Middle layers – capture mid‑level patterns (shapes, motifs).
Deep layers – capture high‑level concepts (semantics, objects).

Through stacked non‑linear transformations, deep learning automatically extracts complex features, driving breakthroughs in image recognition, natural‑language processing, speech recognition, and more. Large language models rely on the Transformer—a deep neural architecture with multi‑head attention and feed‑forward networks—to learn sophisticated language representations.

Plain‑language analogy: Think of deep learning as a series of filters: the first layer detects lines, the second combines lines into shapes, the third recognizes “cat face.” By stacking many layers, a computer can progress from raw pixels to the concept “my cat is sleeping.”

What Is a Large Model?

A Large Language Model (LLM) is a deep neural network whose parameter count reaches billions or even trillions. By self‑supervised pre‑training on massive text corpora, an LLM learns statistical language patterns, semantic representations, and world knowledge, granting it powerful comprehension and generation abilities.

The “largeness” of a model manifests in three dimensions:

Parameter count – the total number of learnable weights and biases. Larger parameter counts increase a model’s capacity to store and represent information. Examples: GPT‑3 has 175 billion parameters; GPT‑4’s exact size is undisclosed but estimated in the trillions; PaLM has 540 billion. More parameters generally correlate with stronger performance, though the relationship is not linear and also depends on data quality, architecture, etc.
Training data scale – large models require terabytes to petabytes of high‑quality text, sourced from web pages, books, papers, code, and more, covering many languages, domains, and styles. Massive data enables the model to learn universal linguistic rules and broad world knowledge.
Computational resources – training and inference demand huge compute budgets: thousands to tens of thousands of GPUs for weeks or months; inference requires substantial memory and processing power; model checkpoints can occupy hundreds of gigabytes.

Core Characteristics of Large Models

Generality – pre‑training yields universal language representations, enabling zero‑shot and few‑shot learning. A single model can handle many downstream tasks (text classification, QA, translation, code generation) without task‑specific retraining.
Contextual understanding – attention mechanisms and long context windows let the model grasp semantic relations, coreference, and logical structure across long passages, far beyond simple keyword matching.
Generation – by sampling from the learned language distribution, the model can produce coherent, fluent, and semantically appropriate text for creative writing, code synthesis, dialogue, etc.

Application Scenarios

Large language models power:

Conversational systems – intelligent chatbots, virtual assistants.
Content creation – article drafting, code generation, creative media.
Machine translation – multilingual, context‑aware translation.
Question‑answering – knowledge‑base or open‑domain QA.
Coding assistants – code completion, explanation, debugging help.
Text analytics – sentiment analysis, summarization, information extraction.
Educational support – personalized tutoring, homework assistance, concept explanations.

Evolution Timeline

2017 – Google introduces the Transformer architecture, laying the groundwork for large models.
2018 – BERT (Bidirectional Encoder Representations from Transformers) appears, boosting comprehension via bidirectional encoding.
2019 – GPT‑2 (1.5 billion parameters) demonstrates strong text generation.
2020 – GPT‑3 (175 billion parameters) showcases zero‑ and few‑shot capabilities.
2022 – ChatGPT (based on GPT‑3.5) launches, refined with RLHF for dialogue.
2023 – GPT‑4 arrives with multimodal abilities and further performance gains.
2023‑present – Open‑source large models proliferate (e.g., LLaMA, ChatGLM).

The trend moves from task‑specific models to universal foundation models, from single‑modality to multimodal, and from closed‑source to open‑source ecosystems.

Model Parameters

What Are Parameters?

Parameters are the learnable weights and biases of a neural network. They dictate how the model transforms inputs into outputs. In LLMs, parameters reside mainly in attention layers, feed‑forward layers, and embedding tables.

Typical large models start at the billion‑parameter scale; for instance, GPT‑3’s 175 B parameters means 175 billion learnable values (1 B = 10⁹).

Parameters consist of:

Weight matrices – connections between neurons across layers, governing information flow.
Bias terms – constant offsets added to each neuron’s activation, adjusting thresholds.
Learned values – obtained through training, encoding statistical language regularities and semantic knowledge.

How Parameters Work

During forward propagation, the model processes an input text step‑by‑step; the current parameter values weight each computation, ultimately producing a probability distribution over possible outputs.

Parameter Count vs. Model Ability

More parameters generally improve capability, but with diminishing returns:

Benefits – higher capacity for complex functions, larger knowledge storage, better generalization (given sufficient data).
Costs – increased compute, memory, and training complexity (often requiring sophisticated distributed training strategies).

Other Factors Influencing Performance

While parameter count is essential, final performance also depends on:

Data quality – scale, diversity, and cleanliness of the training corpus.
Architecture design – depth, hidden dimensions, number of attention heads, etc.
Training strategies – learning‑rate schedules, optimizer choices, regularization techniques.
Alignment & fine‑tuning – supervised fine‑tuning, RLHF, and other methods to steer model behavior.

Thus, model ability = f(parameters, data quality, architecture, training strategy). Parameters are necessary but not sufficient.

Tokens

What Is a Token?

A token is the smallest unit a large language model processes. Before feeding text to the model, it undergoes tokenization, which splits raw text into a sequence of tokens.

Relationship to characters and words:

Character – the smallest written symbol (e.g., a Chinese character, a Latin letter).
Word – a meaningful lexical unit (e.g., “喜欢”, “AI”).
Token – the model’s processing unit; it may be a character, a whole word, a sub‑word piece, or punctuation.

Tokenization Methods

Large models typically use sub‑word tokenization:

BPE (Byte‑Pair Encoding) – iteratively merges the most frequent character pairs to build a vocabulary; balances vocabulary size and token count, handling unknown words gracefully.
WordPiece – similar to BPE but selects merges based on a probabilistic model; used by BERT and related models.
SentencePiece – treats text as a Unicode character stream, enabling unified multilingual tokenization.

Examples:
Chinese “我喜欢AI” might be tokenized as ["我", "喜欢", "AI"] or ["我", "喜", "欢", "AI"]; English “I love AI” could become ["I", " love", " AI"] (note that spaces may be part of a token).

Why Tokenize?

Unified representation – bridges disparate writing systems for multilingual processing.
Handling unknown words – sub‑word tokenization breaks rare words into known pieces, improving generalization.
Efficiency vs. expressiveness – a well‑chosen token split balances vocabulary size and sequence length, preserving semantics while keeping computation tractable.
Model compatibility – architectures like Transformers require fixed‑format token sequences; tokenization is a mandatory preprocessing step.

Tokens and API Costs

Most LLM APIs charge by token usage:

Input tokens – tokens sent to the model.
Output tokens – tokens generated by the model.

Total token consumption = input tokens + output tokens.
Cost example: 100 input tokens + 200 output tokens = 300 tokens billed (input and output may have different per‑token rates).

Estimating Token Counts

Tokenization efficiency varies by language:

Chinese – each character often maps to 1–2 tokens.
English – simple words usually become 1 token; complex words may split into multiple tokens.
Code – tokenization preserves syntactic elements; token count correlates with code complexity.

Exact estimates depend on the specific tokenizer used; different models can produce different tokenizations.

Token Limits (Context Window)

Large models enforce a maximum token count, known as the context window:

Input length limit – e.g., GPT‑3.5 allows up to 4096 tokens; GPT‑4 supports 8192 or 32768 tokens.
Output length limit – a maximum number of tokens per generation request.
Overall limit – input + output must stay within the model’s context window.

How Large Models Understand Text

Large models convert text into numeric vectors for representation and comprehension. This relies on the distributional hypothesis: words with similar meanings occupy nearby positions in vector space. By training on massive corpora, the model learns embeddings where geometric relationships reflect semantic relationships.

Word Embeddings

Word embeddings map discrete tokens to continuous vectors, forming the first layer of representation in a language model. (Content truncated)

Originally written by Li Wei (李唯_) and published in Chinese on 后端技术栈全书 (Full-Stack Backend Engineering). Translated and adapted for DriftSeas with permission.