With the public's in-depth understanding of ChatGPT, large models have become the focus of everyone's research and attention. However, the reading threshold for the content written by many practitioners is indeed too high and the information is scattered. It is really not easy to understand for people who do not know much about it, so I will explain it one by one here, hoping to help those who want to understand related technologies. Readers have a general understanding of large models, AI large models, and ChatGPT models.
1. Large model
1.1 What is a large model?
Large model is the abbreviation of Large Language Model . A language model is an artificial intelligence model that is trained to understand and generate human language . "Large" in "large language model" means that the number of parameters of the model is very large .
Large models refer to machine learning models with large parameter sizes and complexity. In the field of deep learning, large models usually refer to neural network models with millions to billions of parameters . These models require a large amount of computing resources and storage space to train and store, and often require distributed computing and special hardware acceleration technology.
Large models are designed and trained to provide more powerful and accurate model performance for more complex and larger data sets or tasks. Large models are usually able to learn more subtle patterns and rules, and have stronger generalization and expression capabilities.
Simply put, it is a model trained with big data models and algorithms, which can capture complex patterns and patterns in large-scale data, thereby predicting more accurate results. If you still can’t understand it, it’s like fishing for fish (data) in the sea (on the Internet), fishing for a lot of fish, and then putting all the fish into a box. Gradually, a pattern will be formed, and finally the prediction will be possible, which is equivalent to a It is a probabilistic problem. When the data is very large and has regularity, we can predict the possibility.
1.2 Why a larger model is better
A language model is a machine learning model that uses statistical methods to predict the likelihood of a sequence of words appearing in a sentence or document . In a machine learning model, the parameters are part of the machine learning model in the historical training data. Learning the model in the early stages is simpler, so there are fewer parameters. But these models have limitations in capturing distance dependencies between words and generating coherent and meaningful text. Large models like GPT have hundreds of billions of parameters, which are much "bigger" than early language models. A larger number of parameters allows these models to capture more complex patterns in the data they are trained on, allowing them to generate more accurate models.
2. AI large model
2.1 What exactly is an AI large model?
AI large model is the abbreviation of "artificial intelligence pre-trained large model". AI large model includes two layers of meaning, one is "pre-training" and the other is "large model". The combination of the two produces a new artificial intelligence model, that is, the model is completed on a large-scale data set After pre-training, no fine-tuning or only a small amount of data is required to directly support various applications.
Among them, pre-trained large models are like students who know all the basic knowledge and have completed general education, but they still lack practice. They need to practice and get feedback before making fine adjustments to complete the task better. We still need to continue to train it so that it can be used better by us.
2.2 Advantages of large AI models
2.2.1 Contextual understanding ability
AI large models have stronger context understanding capabilities and can understand more complex semantics and contexts. This allows them to produce more accurate and coherent responses.
2.2.2 Language generation ability
Large AI models can generate more natural and fluent language, reducing errors or confusing issues when generating output.
2.2.3 Strong learning ability
Large AI models can learn from large amounts of data and use the learned knowledge and patterns to provide more accurate answers and predictions. This makes them better at solving complex problems and responding to new scenarios.
2.2.4 High portability
The learned knowledge and abilities can be transferred and applied in different tasks and fields. This means that the model can be applied to multiple tasks with one training session without the need to retrain.
2.3 Which domestic companies have large models?
At present, domestic companies such as Baidu, Alibaba, Tencent, and Huawei have large AI models, but each model series has its own focus. Baidu has been deploying AI for many years and has a certain first-mover advantage with large models. Baidu’s Wen Xinyiyan’s API call service has been tested by hundreds of millions of companies. In terms of large industry models, it has been used in case studies with State Grid, Shanghai Pudong Development Bank, and People's Daily Online.
Alibaba Tongyi's large model is good at logical operations, coding capabilities, and voice processing. The group has a rich ecosystem and product online, and is widely used in travel scenarios, office scenarios, and shopping scenarios.
3. GPT model
3.1 What is the GPT model?
Generative pre-trained Transformer models, commonly referred to as GPT, are a family of neural network models using the Transformer architecture and are a key advancement in artificial intelligence (AI) that powers generative AI applications such as ChatGPT. The GPT model enables applications to create human-like text and content (images, music, etc.) and answer questions in a conversational manner. Organizations across industries are using GPT models and generative AI for Q&A bots, text aggregation, content generation and search.
The GPT model is a deep learning model that uses the Transformer architecture and can be used for natural language processing (NLP) tasks. It learns the statistical laws and semantic associations of language from a large amount of Internet text through a large-scale pre-training process. During the pre-training process, GPT is able to capture long-distance dependencies through a multi-layer self-attention mechanism, and can effectively model context information.
3.2 Application areas
Automatic text generation: Excellent performance in automatic text generation. By inputting a piece of text, you can generate coherent and reasonable subsequent content. This feature gives it great potential for tasks such as automatic writing, machine translation, and chatbots.
Semantic understanding: In-depth semantic understanding of text can be achieved by studying contextual information in a large number of corpora. It can understand and explain complex questions and provide logical answers. This feature makes it widely used in fields such as question and answer systems, intelligent assistants, and information summarization.
Sentiment analysis and public opinion monitoring: The emotional color in the text can be analyzed and classified according to its emotional tendency. This ability makes it an important tool in fields such as social media public opinion monitoring, user comment analysis, and emotional intelligence systems.
As a huge pre-trained language model, it has a revolutionary impact on the field of natural language processing. Its applications in automatic text generation, semantic understanding, sentiment analysis, and public opinion monitoring have already achieved initial results. Although there are still challenges and limitations, we have every reason to believe that the future GPT model will continue to develop and play an increasingly important role in the field of artificial intelligence.
Reprinted from: Baidu Security Verification