Large models refer to machine learning models with a large number of parameters and complex computational structures. This article starts from the basic concept of large models, distinguishes related concepts that are easily confused in the field of large models, and provides a detailed interpretation of the development history of large models, serving as a reference for understanding the basic knowledge of large models.
1. Definition of Large Models
Large models are machine learning models characterized by a large number of parameters and complex computational structures. These models are typically constructed using deep neural networks and contain billions or even trillions of parameters. The design of large models aims to enhance the model’s expressive power and predictive performance, enabling them to handle more complex tasks and data. Large models have widespread applications in various fields, including natural language processing, computer vision, speech recognition, and recommendation systems. By training on massive datasets, large models learn complex patterns and features, exhibiting stronger generalization capabilities, allowing them to make accurate predictions on unseen data.
ChatGPT provides a more accessible explanation of large models, showcasing a human-like ability to generalize and reason: a large model is essentially a deep neural network trained on vast amounts of data, where its enormous data and parameter scale result in the emergence of intelligence, displaying human-like cognitive abilities.
So, what is the difference between large models and small models?
Small models typically refer to models with fewer parameters and shallower layers. They offer advantages such as being lightweight, highly efficient, and easy to deploy, making them suitable for scenarios with smaller datasets and limited computational resources, such as mobile applications, embedded devices, and the Internet of Things.
As the training data and parameters of a model continue to expand, reaching a certain critical scale, they exhibit some unpredictable and more complex capabilities and characteristics. The model can automatically learn and discover new, higher-level features and patterns from the original training data; this ability is known as “emergent capability.” Machine learning models with emergent capabilities are considered large models in their own right, which is the most significant distinction from small models.
Compared to small models, large models generally have more parameters and deeper layers, offering stronger expressive power and higher accuracy, but they also require more computational resources and time for training and inference, making them suitable for scenarios with large datasets and ample computational resources, such as cloud computing, high-performance computing, and artificial intelligence.
2. Distinguishing Related Concepts of Large Models:
Large Model (also known as Foundation Model) refers to machine learning models with a large number of parameters and complex structures, capable of handling massive data and performing various complex tasks, such as natural language processing, computer vision, and speech recognition.
Super Large Model: Super large models are a subset of large models, with parameter counts far exceeding those of large models.
Large Language Model: This typically refers to natural language processing models with large-scale parameters and computational capabilities, such as OpenAI’s GPT-3 model. These models can be trained on vast amounts of data and parameters to generate human-like text or answer natural language questions. Large language models have widespread applications in natural language processing, text generation, and intelligent dialogue.
GPT (Generative Pre-trained Transformer): Both GPT and ChatGPT are language models based on the Transformer architecture, but they differ in design and application: GPT models are designed to generate natural language text and handle various natural language processing tasks, such as text generation, translation, and summarization. They are typically used in a unidirectional generation context, generating coherent outputs based on given text.
ChatGPT: ChatGPT focuses on dialogue and interactive conversation. It has undergone specific training to better handle multi-turn dialogues and context understanding. ChatGPT is designed to provide a smooth, coherent, and engaging conversational experience, responding to user inputs and generating appropriate replies.
3. Development History of Large Models
Emergence Period (1950-2005): Traditional Neural Network Models Represented by CNN
· In 1956, the concept of “artificial intelligence” was proposed by computer expert John McCarthy, marking the transition of AI development from initially being based on small-scale expert knowledge to one based on machine learning.
· In 1980, the prototype of convolutional neural networks, CNN, was born.
· In 1998, the basic structure of modern convolutional neural networks, LeNet-5, was developed, transitioning machine learning methods from early shallow machine learning models to deep learning models, laying the foundation for in-depth research in fields like natural language generation and computer vision, and pioneering the iteration of subsequent deep learning frameworks and the development of large models.
Exploration and Consolidation Period (2006-2019): New Neural Network Models Represented by Transformer
· In 2013, the natural language processing model Word2Vec was introduced, proposing the “word vector model” that converts words into vectors, enabling computers to better understand and process text data.
· In 2014, GAN (Generative Adversarial Network), hailed as one of the most powerful algorithmic models of the 21st century, was born, marking a new phase in the study of generative models in deep learning.
· In 2017, Google introduced the revolutionary neural network structure based on self-attention mechanisms—the Transformer architecture, laying the groundwork for large model pre-training algorithms.
· In 2018, OpenAI and Google released GPT-1 and BERT large models, respectively, marking the mainstream adoption of pre-trained large models in the field of natural language processing. During this exploration phase, the new neural network architecture represented by Transformer laid the algorithmic foundation for large models, significantly enhancing the performance of large model technology.
Rapid Development Period (2020-Present): Pre-trained Large Models Represented by GPT
· In 2020, OpenAI launched GPT-3, with a model parameter scale reaching 175 billion, becoming the largest language model at that time, achieving significant performance improvements in zero-shot learning tasks. Subsequently, more strategies, such as Reinforcement Learning from Human Feedback (RLHF), code pre-training, and instruction fine-tuning, emerged to further enhance reasoning abilities and task generalization.
· In November 2022, ChatGPT, equipped with GPT-3.5, made its debut, quickly igniting the internet with its realistic natural language interactions and multi-scenario content generation capabilities.
· In March 2023, the latest released super large-scale multimodal pre-trained model—GPT-4—demonstrated multimodal understanding and the ability to generate various types of content. During this rapid development period, the perfect combination of big data, big computing power, and big algorithms significantly improved the pre-training and generative capabilities of large models, as well as their multimodal and multi-scenario application capabilities. The immense success of ChatGPT is attributed to the powerful computing capabilities of Microsoft Azure and the support of vast datasets like Wikipedia, achieved through fine-tuning strategies based on the GPT model and reinforcement learning from human feedback (RLHF) on the Transformer architecture.
