What Is a Large Language Model (LLM)? A Comprehensive Guide in One Article

In the past two years, the technology circle artificial intelligence, AI has become the industry's popular, "large model ( Large Language Model )" word also often appear in our vision. For ordinary people, while watching and eating melon will certainly have doubts, GPT, artificial intelligence, AI, large model, each of these words can read and understand, but connected but feel incomplete understanding. Today we will talk about what the "large language model" is, why is it so amazing?

Online introduction of large models of various, not very comprehensive, this article is a more complete introduction to what is a large model, the characteristics of large models, large models of classification, large models of how to practice, as well as the development of large models of the existence of difficulties and challenges, can be applied in the direction of the content of the longer, but for the learning to understand the large model must be to understand these.

What is a large language model?

Background.

When AI began to develop in the middle of the last century, there were three schools of thought, one of which was "connectionism". This school of thought believed that the key to realizing AI was to mimic the structure of the human brain, such as neurons, which evolved into the now-common artificial neural network model. From this point of view, the emergence of large models is not accidental, and artificial neural networks are very common in the use of large models, and the two have a tendency to promote each other and develop together, readers interested in the book on the historical development of artificial intelligence related chapters.

Human intelligence activities are very complex, take language as an example, which involves a lot of knowledge and reasoning. For example, when reading an article, one needs to understand the statements, recognize the syntax and semantics, infer the logical relationships, and combine these information to form the meaning of the whole article . Similarly, when having a conversation one needs to understand the other party's intentions and emotions and respond based on them. Big Language Models can model and solve these tasks by learning massive amounts of text data to simulate these complex processing and reasoning processes. This could not only help improve existing Natural Language Processing (NLP) techniques, but also provide new tools for deeper understanding and use of natural language. Exploring and understanding the nature of human intelligence through the development of AI is one of the original purposes of AI.

Let's talk about the definition of the word "emergence", which we often come across. In order to facilitate your understanding, let's take an example, newborns learn to speak basically in one year to one and a half years old, although many times are unknown single words, but the early in a large number of listening, learning and understanding process, suddenly one day began to speak, and can understand what adults say, based on this and express their own ideas, this phenomenon can be regarded as the human language skills "emergence".

Similarly, in the field of artificial intelligence, the computer through natural language processing technology, deep learning model parameters accumulated to a certain amount, it will realize the "emergence" ability, undeniably, early pre-training model can not do or do not do well, such as the past in the NLP of the text generation, text comprehension, automated Q&A and other downstream tasks. In the big language model not only generates more fluent text, the authenticity of the content has also improved significantly. Of course, it is still uncertain whether large language model can eventually lead to "general artificial intelligence (AGI)", but at present, large language model do have the hope of leading the next heavyweight AI track, which is one of the reasons why large language model have been hot out of the circle in recent years.

Model Definition

We have often used many models in our lives, such as the ice-cream molds for homemade ice-cream, the cake models displayed in cake stores, the bowls with egg custard, and so on, all of which are molds that we can use to make the final product more simple.

To take a further example, when we make a dish, we usually have seasonings such as oil, salt, sauce, vinegar, flavor powder, and various main ingredients and auxiliary ingredients, and then according to a certain fire and time and operation techniques, we can make a delicious dish. If you are opening a restaurant, guests and more, will certainly prepare these materials in advance, and then guests order according to the menu, you get the name of the dish ordered, you know in accordance with what form to make this dish.

Here the name of the dish is equivalent to a model, as long as you know the name of the dish, you know the main ingredients needed, auxiliary materials, heat, time and operation techniques, made (output) you this dish, of course, as a chef you must have trained/practiced (training) in advance how to do this dish.

The previous talk is all about models of the physical, mapping to the virtual includes mathematical modeling which we have heard many times:

I need to calculate a lot of squares of a number and then subtract 3. It's too much trouble to calculate them one by one, so I can use a number, calculate its square and then subtract 3. Based on this example, building a model becomes a virtual "mold", which I can use to calculate all my other calculations. I can then use this "mold" to compute my other data.

In computing, a model usually consists of inputs, parameters and outputs. In the above example, the data you need to compute is the input, the three subtractions are the parameters that can be adjusted, and the final result you get is the output.

Combined with the abstract meaning of the model: through the subjective consciousness with the help of physical or virtual manifestation, constitutes the objective elaboration of the morphological structure of an object to express the purpose of the object (object is not the same as the object, is not limited to the physical and virtual, is not limited to the plane and three-dimensional). You can better understand the meaning of modeling.

Then finally to today's topic, the large language model, literally, more than the model of a big word, where the big, basically contains the following meanings:

• Large number of parameters

Big models have a large number of model parameters, which can be billions (Billions) or even hundreds of billions (Trillions). For example, some advanced language models may have more than 10 billion parameters.

• Model Complexity

Due to the large number of parameters, large models can capture and learn very complex patterns and relationships in the data.

• Large amount of training data

Big models are often trained on large-scale datasets that may contain billions of words or more, allowing the model to learn rich linguistic and world knowledge.

• Large computational resources

Training and running large models requires large computational resources, including high-performance GPUs or TPUs, large amounts of storage space, and efficient computational frameworks.

• Emergent capabilities

As mentioned earlier, large language model may exhibit complex capabilities that are not explicitly programmed and that seem to emerge naturally as the size of the model increases.

One sentence summarizes large language model:

Massive amounts of data, through natural language processing and machine learning, deep learning and other algorithms or learning methods, on a large number of computational resources, trained a kind of computer software model, this model has a huge number of parameters, reflecting the emergence of artificial intelligence ability, in the development stage of artificial intelligence reflects a strong applicability.

Big model ≠ artificial intelligence, large language model is only a practical means of artificial intelligence technology development route on the cutoff at present, and the current large language model performance is still in the stage of weak artificial intelligence, more on the explanation of artificial intelligence, you can see the book this "what is artificial intelligence

What can large language model do?

So what do large language model do? The main reason why large language model are so lively is that their application scenarios are very wide, covering almost all fields that require natural language processing. Here are a few examples of typical application scenarios:

Natural Language Processing (NLP)

Text generation and summarization: articles, reports, summaries and emails can be automatically generated.

Machine translation: for translating one language into another.

Sentiment analysis: helps companies understand how customers feel about their products or services.

Question and Answer System: Provides fast, accurate answers for customer service, education and technical support.

Content Creation

Creative writing: assists in the creation of novels, screenplays and poems.

News Writing: quickly generate press releases and stories.

Advertising and marketing: create engaging advertising copy and marketing materials.

Data Analysis

Data Interpretation: Help users understand complex data sets.

Trend Forecasting: Analyze data to predict market trends.

Automated Reporting: Generate regular data reports.

Education and Assisted Learning

Personalized Instruction: Provides customized content based on students' learning progress and style.

Homework Help: Helps students answer questions and complete assignments.

Language Learning: Provides language practice and conversation practice.

Software Development

Code Generation and Completion: Helps developers write code faster.

Error Detection: Identifies potential errors in code.

Automated Testing: Generate test cases and test code.

Games and Entertainment

Character dialog: Generate natural dialog for game characters.

Storyline: Design the story line and plot of the game.

Personalized Experience: Adapt game content to player behavior.

Healthcare

Clinical Documentation Generation: Help doctors record and generate medical history reports.

Diagnostic assistance: Analyze medical documents to assist diagnosis.

Patient Counseling: Provide initial medical counseling and information.

Legal & Consulting

Contract Review: Analyze key clauses in contracts.

Legal Research: Help lawyers quickly find relevant legal information.

Consulting Services: Provide preliminary legal consulting services.

What are model parameters?

Or take the previous example of cooking, if you do a dish can be operated with less space, then do out of the flavor of the dish must be very single, can not meet the tastes of people in different regions (Shanghai people like like sweet, Hunan like spicy, Sichuan like spicy), the more seasonings you give you, the more sufficient operating time, coupled with the different fire, containers, etc., you can make a better and more symbolic requirements according to the requirements of the dish, and may also be able to create You may even be able to create your own unique flavor.

The various selection factors mentioned above are parameters in a large model, and the larger the model parameters, the stronger the reasoning ability, and the increase in the number of model parameters can be compared to the growth and maturity of the human brain itself.

Model parameters are variables such as weights and biases that can be learned in machine learning and deep learning models. During training, these parameters are tuned by optimization algorithms (e.g., gradient descent) to minimize the gap between the model's predicted and actual values. The initial values of the parameters are usually random, and as training proceeds, they gradually converge to appropriate values that capture the complex patterns and relationships in the input data .

In large models, the number of parameters is usually very large. As an example, OpenAI's GPT-3 model has about 175 billion parameters, enabling it to perform more complex tasks such as natural language generation, translation, summarization, and so on. The large number of parameters gives the model greater representational power , but it also comes with higher computational costs and memory requirements. This is why large models usually require special hardware resources (e.g., GPUs or TPUs) and optimization strategies (e.g., distributed training and mixed-precision training) for effective training.

How are large models trained?

The parameters of a large model can be automatically tuned through the training process to capture complex relationships in the input data. Such models usually have deeper network structures and more neurons to increase the model's representational and learning capabilities.

Large language model training is divided into three main steps:

The first step, unsupervised learning

With a large amount of data, unsupervised pre-training is performed to obtain a base model that can perform text generation.

For example, the training data of the base model GPT-3, there are several Internet text corpus, covering news, books, papers, Wikipedia, social media, etc., the training data is 300 billion tokens (an article to see what is a token).

With a large amount of trainable data, the model is trained using unsupervised learning (the amount is so huge that human supervision is impossible), learning the syntax and semantics of human language on its own, and understanding the structure and pattern of expression. Then comes the ability to predict what comes after based on the context of the model, and to update the weights of the predictions based on specific answers, thus predicting what comes after reasonably well based on what comes after. With more and more training, the ability to generate gets better and better.

Most of the core mechanisms for training in this are using the Transformer deep learning model architecture, about which you can read the article "Illustrating the Principles of Transformer" if you want to know more about it.

This step is the most time-consuming, costly arithmetic, burning money, light GPT-3, it took several months, hundreds of V100 GPUs, millions of dollars in costs.

The second step, supervised learning

The base model is monitored and fine-tuned with some high-quality dialog data written by humans to get a fine-tuned model that has better dialog capabilities in addition to continuing the text.

The base model obtained in the first step of training is not ready for use, for example, it does not have the ability to use our common conversations, this time we need to fine-tune the base, show the model a lot of conversation data (language model as an example), to get a fine-tuned base model, so that the model is more adapted to a specific task.

This stage of training compared to the base model training requires less data generalization, training time is short, the cost is much lower, this stage of the model is no longer from the massive amount of data inside the learning, but from the human written professional high-quality dialogues to learn, equivalent to both give the model the question and give the model human-approved answers, belongs to the supervised learning, also known as supervised fine-tuning (SFT, Supervised Fine-Tuning) after the completion of the model to get a more adaptive to the specific task, so that the model more adapted to the specific task. -Tuning) to get a SFT model after completion.

The third step, training reward model + reinforcement learning training

Using questions and multiple corresponding answer data, human annotators are asked to rank the quality of conversations, and then based on these data, a reward model that can score predictions of the answers is trained. Then use the reward model to rate the generated responses to the questions using the model obtained in the second step, and use the ratings for feedback for reinforcement learning training.

This is somewhat analogous to training a puppy, where the puppy, as it interacts with the trainer, realizes that certain actions earn it food and certain actions earn it punishment, and by observing the relationship between the actions and the punishments, the puppy gradually trains to be what the trainer expects it to be.

To make the model the same, and to achieve what humans want, it is the same thing, let the model answer the questions, and then evaluate the questions (3H principle: Helpful-helpfulness, Honest-truthfulness, and Harmless-harmlessness), but relying on humans to evaluate is too inefficient, so we first train a reward model to be used for evaluation. Relative to humans to evaluate, the efficiency is greatly improved, through the big reinforcement learning, and eventually the large language model is practiced.

You can watch this video to understand the training process of ChatGPT :

Main techniques used for large language model

The large language model uses many advanced techniques, mainly including the following:

• Deep Neural Networks (DNNs)

Big models typically use deep neural networks with multiple hidden layers to capture higher-order features and abstract concepts in the input data.

• Convolutional Neural Networks (CNNs)

Convolutional Neural Networks are commonly used for large models in computer vision tasks. With designs such as local sensory fields, weight sharing, and pooling operations, CNNs can efficiently process image data and extract visual features at multiple scales.

• Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)

In sequential data processing tasks (e.g., natural language processing and speech recognition), large models may employ recurrent neural networks or their variants (e.g., Long Short-Term Memory networks) to capture temporal relationships.

• Transformer Architecture

Transformer is a Self-Attention Mechanism (SAM) neural network architecture that is widely used for large models in natural language processing.Transformer can process all elements of the input sequence in parallel, which greatly improves the efficiency of model training.

• Pretraining and Fine-tuning

In order to fully utilize a large number of parameters, large models are usually first pre-trained on large-scale datasets to learn a generalized feature representation. Then, they are fine-tuned on task-specific datasets to adapt to specific application scenarios.

• Distributed Training and Mixed Precision Training

To handle the computational and storage requirements of large models, researchers have adopted several efficient training strategies such as Distributed Training (distributing the model and data across multiple devices or nodes for parallel computation) and Mixed Precision Training (utilizing numerical representations with different accuracies in order to reduce computational and memory resource requirements).

Together, these techniques and strategies have supported the development and application of large models to achieve excellent performance in a variety of complex tasks. However, large models also pose challenges in terms of training cost, computational resources, and data privacy.

Classification of Large Models

The development of models was initially accompanied by the continuous development of natural language processing technology, due to the fact that textual data is much larger and more accessible. Therefore, the biggest classification of large language model at present is still big language models, and in the past two years, some large language model fusing language with other forms have been derived, for example, text-generated music (MusicLM), text-generated images (DALL-E2, Midjourney), text-image-generated robot actions (RT-1), text-generated videos (Sora), etc.

Big models include, but are not limited to, the following categories:

Big Language Models

Specialize in processing natural language and are capable of understanding, generating, and processing large-scale textual data. Big language models have achieved significant results in tasks such as machine translation, text generation, and dialog systems.OpenAI's GPT series is representative of this, including the latest GPT-4.

Vision Big Models

Specializing in computer vision tasks such as image classification, target detection, image generation, etc., video generation. They are capable of extracting information about objects, scenes and structures from images. For example, Vision Transformer (ViT) is a vision macromodel based on a self-attention mechanism for image classification tasks, while Diffusion Transformer (DiT) is a deep learning model that combines the diffusion model and the Transformer architecture, which gradually removes the noise and reverses the process to produce high-quality images, and is particularly good at handling complex image patterns and details.

Multi-Modal Large Models

Able to process many different types of data, such as text, images, audio, etc., and create correlations between them. Multimodal macromodels excel in tasks involving multiple perceptual inputs, such as text-graph fusion and image description generation. Multimodality is the next big trend in the development of large language model.CLIP (Contrastive Language-Image Pre-training) is a multimodal large language model that understands both text and images for tasks such as image classification and natural language reasoning.

Decision Macromodel

Focused on making decisions and planning, they are often used in areas such as reinforcement learning. They are capable of making intelligent decisions in the face of uncertainty and complex environments. Models in Deep Reinforcement Learning, such as AlphaGo and AlphaZero, are representative of Decision Grand Models and are capable of achieving superhuman levels of performance in games such as Go.

Industry Vertical Big Models

Specially designed for tasks in specific industries or domains, such as medicine, environment, education, etc. They usually excel in handling domain-specific data and problems. In the medical field, large-scale medical image processing models are used for diagnosis and analysis. In finance, models may be used for risk assessment and trading strategies.

Challenges and Difficulties of Big Models

Big models in 2023 suddenly blossomed everywhere, blowout development, especially in the latter half of the year, almost most technology companies, academic groups, research institutions, and student teams are releasing their own large language model, it feels that large language model suddenly from the Loyang paper expensive to readily available, 24 years to start a lot of projects about the large language model aspects, is the current (as of the first half of 2024) the direction of the capital of the pro-favorable. At the same time, there are some difficulties and challenges in large models, which can be roughly summarized from three aspects.

Training Costs

As mentioned in the previous section on how to train large models, the scale and complexity of large models require large amounts of computing resources for training and inference. It is usually necessary to use high-performance computing units, such as GPUs (Graphics Processing Units) or TPUs (Tensor Processing Units) to support the operation of large models.

Arithmetic power, data, and algorithmic models together build the development ecology of large model applications, and the relationship between the three is also interdependent and indispensable. The adequacy of arithmetic power directly affects the training speed and model size of the large language model, more powerful arithmetic power can support larger models, longer training time, and higher training accuracy; the diversity, quality, and size of the data have a significant impact on the performance and generalization ability of the large language model, and rich data can help the model better understand the different contexts and problems, and improve the model's performance; the improvement of algorithmic models can reduce the need for computing power and data, allowing models to be trained more efficiently or achieve better performance with limited data.

Cost of data

The more and more complete the training data, the better the effect of the model training will be, the more powerful the emergence of the ability can be, but for the training of the data cost is more, the need for a huge scale, type of diverse, fast, low value density of the collection of data, it is beyond the scope of the ability of the traditional data processing software, the need for new technologies and methods to analyze and utilize:

• Large volume (Volume) :

The volume of data in Big Data is so huge that it is usually measured in terabytes (terabytes), petabytes (petabytes) or eigabytes (exabytes). For example, according to statistics, the amount of data generated by global Internet users in 2020 reached 59ZB (zettabytes), which is equivalent to generating 16 billion gigabytes of data every day.

• High Speed (Velocity)

The data flow of Big Data is very fast and needs to be collected, processed and analyzed in real time or near real time. For example, billions of tweets, WeChat and other social media messages flow across the web every day, millions of search requests occur on search engines every second, and thousands of hours of video are uploaded on video platforms every minute.

• Variety

The types of data in Big Data are very diverse, including structured data (e.g., numbers, text, etc.), semi-structured data (e.g., XML, JSON, etc.), and unstructured data (e.g., images, audio, video, etc.). These data come from different sources such as sensors, logs, social media, web pages, documents etc.

• Value

The value density of big data is relatively low, meaning that only a small percentage of it contains useful information that needs to be mined through effective analytics. For example, only part of the information about a face or object in a photo may be valuable, while the rest of the background or noise is useless.

• Real (Veracity)

The authenticity and reliability of big data is also an important issue because there may be inaccurate, incomplete, or duplicate data in big data, which can affect the quality of the data and the results of the analysis. Therefore, big data needs to be cleaned, integrated, and other operations to improve veracity.

Arithmetic cost

The size and complexity of large language model, they require a lot of computational resources for training and inference. It is often necessary to use high-performance computing units such as GPUs (Graphics Processing Units) or TPUs (Tensor Processing Units) to support the operations of large language model. These hardware are very expensive, with NVIDIA A100 and H100 chips selling for more than $200,000 per chip in the first half of 2024, and thousands of similar chips are needed for tour training of large models.

On the other hand, the training model usually takes months or even years, which is also very huge power consumption. These costs for a business need to burn a lot of capital.

Algorithms

Improvements in algorithms also have a very large impact on the cost of training, including the cost of time, and AI has been rapidly advancing on large language model in recent years, mainly the Transformer architecture proposed in 2017 on natural language reasoning, which breaks through the limitations on recurrent neural networks (RNN) and long and short-term memory networks (LSTM).

The improvement of the algorithm is mainly dependent on the talent pool, the development of artificial intelligence, the demand for talent is now very high, and the price of the recruitment gallop market is also rising.

Because of these costs in , so in the commercial landing on the use of the need to use large models are more expensive, the general staff can only test to pay a fresh, can not get a wide range of spread use. The future is a very important direction of research is how to reduce costs, on this basis, commercialization and application in order to spread a large area.

Capability Boundary

The powerful expressive ability of the large language model makes it have almost the same understanding of all kinds of problems as human beings, and it has amazing generation and comprehension ability, which can generate new information according to the internal representation, such as image, sound, text, etc., and can understand complex language phenomena such as extra-verbal meaning, metaphor, humor, etc. The large language model also has the unique ability of human beings to generate new information.

The Big Model also has a uniquely human creative learning; the Big Model has the potential to learn to reason and plan, to reason and make decisions based on goals, and to interact with the environment through feedback, and even to shape the environment. It is able to speculate about the future based on existing data and information, and can create various types of works that fit the description.

The Problem of Illusion

Although as of now the capabilities of large language model are recognized by humans, there are a series of problems, such as the often criticized problem of illusion, where large language model often lack common sense and morality and may produce fictional, erroneous, or harmful outputs.

Because large language model rely on training data, often the data and the corpus are outdated, and cannot be supplemented with information about problems in specialized areas and what is happening in reality in real time.

In addition, current large language model are pre-generative reasoning, able to reason about contextual information but unable to do logical relationships, such as simple mathematical operations, understanding of the real physical world, etc.

Repetition problem

In addition to the illusion problem, the large language model also has the problem of duplicating the output content, sometimes the large language model will appear to lack of creativity and imagination, it will duplicate the output of similar content, or copy the existing content.

Discrimination and bias

In foreign countries, the most unacceptable thing about the output content of large language model is the problem of prejudice, we know that foreign countries are more sensitive to ethnic and gender discrimination, and large language model lack self-awareness and emotional expression, often appear some discriminatory content, and will generate paranoid content, so that people think it is a lack of humanity and empathy.

Application landing

Insufficient industry knowledge

Data is like the "nutrients" of a large language model, which directly determines whether the model can grow well and play a useful role. Whether at the level of basic research or industrial application, access to sufficient and diverse high-quality data is a key element in cultivating powerful AI models.

Compared to the current many general large models, are individual head of the technology company trained models, in the industry knowledge, corpus is insufficient, it is difficult to solve the complex tasks encountered in the industry, so the general large model for the industry, you need to do the second pre-training, in order to be truly used in the industry up!

Insufficient application experience

As a result of the rapid development of large models in recent years, every day is different, the technology is changing too fast, supporting the relevant artificial intelligence technology education system can not keep up, a large number of talent gaps (some time ago, Li Yizhou incident can be feedback on how strong the demand for training and education in the community is).

At the same time, the emergence of large models is still a relatively new technology, although the ability is very powerful, but in the actual commercial landing on the use of relatively few, no precipitation related to the landing experience, so that no matter what the enterprise and individuals, in the use of large models above the challenge of groping for stones to cross the river to try to use.

High threshold of use

Big model in the ordinary person seems to use the threshold is still very high, although the people around have heard of the large language model, ChatGPT, Wenxin Yiyin these key words (more key words to learn can see this article "learning AI must understand the technical key words at a glance"), in fact, the real understanding of very few people, the use of most of them will only use the chat box to talk with the model, the reasons can be summarized as follows The reason can be attributed to the following aspects:

Technical complexity

Large models are usually built based on complex deep learning techniques and a lot of mathematical knowledge, which requires users to have at least some background knowledge of machine learning and deep learning to understand how the model works and the impact of parameter tuning on its performance.

Computational Resource Requirements

Large models require a lot of computational resources for training and inference. Acquiring and maintaining high-performance computing hardware (e.g., GPU clusters) is difficult and costly for the average person.

Data Processing and Preparation

Cleaning, formatting, and preprocessing of data is required before using large models, which usually requires specialized data science knowledge and skills. The average user may lack experience in working with large and complex datasets.

Model Tuning and Optimization

Large models often require careful tuning to achieve optimal performance, which includes hyper-parameter tuning, fine-tuning of the model, and so on. These operations require specialized knowledge and can be challenging for non-experts.

Software and tool understanding

Large models usually require the use of specific deep learning frameworks and tools such as TensorFlow, PyTorch, etc. Learning how to use these tools also requires time and effort.

To lower these barriers, there is a need to develop more user-friendly and easy-to-use tools and platforms with more intuitive and simple interfaces, as well as more educational and training resources to help ordinary people better understand and apply large language model.

What are the current mainstream large language model (as of the first half of 2024)

Foreign large language model

GPT ( Generative Pre-trained Transformer ) series

Developed by OpenAI, including GPT, GPT-2, GPT-3 and the latest GPT-4, these models have a wide range of applications in the field of natural language processing, such as text generation, Q&A systems, etc.

The most influential one is GPT4 (Generative Pre-trained Transformer 4), including 4.0 default model; 4.0 networking model; 4.0 data online analysis model; 4.0 plug-in model; 4.0 image generation model

Compared with other language models, ChatGPT 4 has several significant features:

1. More powerful language comprehension: ChatGPT 4 uses the latest self-supervised learning method, which can automatically learn richer and more accurate language knowledge from a large amount of unlabeled data, thus improving the model's language comprehension ability.

2. Higher quality of text generation: ChatGPT 4's generation capability has been further improved to generate more natural, fluent and creative texts, such as automatic writing, automatic dialog, automatic translation and so on.

3. Greater Efficiency and Scalability: ChatGPT 4 has been optimized for both training and inference to handle larger datasets and more complex tasks, while also improving the computational efficiency and scalability of the model.

4. More Transparent and Interpretable: The internal structure and parameters of ChatGPT 4 can be more clearly explained and understood, allowing for better model tuning and improvement.

LLaMA Series

The LLaMA series is a series of Large Language Model Meta AI released by Meta AI (Facebook's parent company) to provide efficient and high-performing language models.

There are several versions of the LLaMA model with parameter counts ranging from 700 million (7B) to 65 billion (65B). According to, the LLaMA model with 13 billion parameters outperforms GPT-3 with 175 billion parameters in multiple benchmarks and can run on a single V100 GPU. The largest LLaMA model with 65 billion parameters rivals Google's Chinchilla-70B and PaLM-540B.

The open source nature of LLaMA models fosters the AI community, allowing researchers and developers to experiment and innovate freely.Meta also provides a Responsible Use Guide to instruct developers on how to safely use these models, and has now open sourced some of the LLaMA3 parameter models.

Claude Series

The Claude Series is a series of large-scale language models developed by Anthropic, an artificial intelligence research lab founded by several former OpenAI team members dedicated to developing reliable, interpretable, and safe AI systems.The Claude Series of models were designed with special considerations for safety and interpretability, aiming to reduce the biases and inaccuracies common in large-scale language biases and inaccuracies that are common problems in models.

Features of the Claude family of models include:

• Security: The Claude family was designed with a focus on reducing harmful outputs and improving the security of the models.

• Interpretability: These models are designed to provide better interpretability to help researchers and developers understand the decision-making process of the model.

• Multi-tasking capabilities: The Claude family of models typically performs well on a wide range of natural language processing tasks, including text generation, question and answer, and text categorization.

• Model Scale: Claude models are available in several different scale versions to accommodate different application requirements and computational resource constraints.

• Open Source Collaboration: Although some of Anthropic's research is closed source, the company also participates in open source collaborations to share some of its research with academia and industry.

• Continuous Iteration: The Claude family of models is constantly being iterated and updated to improve performance and address emerging issues.

BERT (Bidirectional Encoder Representations from Transformers)

Developed by Google, the Bidirectional Transformer model with 340 million parameters has made important breakthroughs in the field of natural language processing, and is widely used in tasks such as text categorization and named entity recognition.

T5 (Text-to-Text Transfer Transformer)

A general-purpose text-to-text transfer model proposed by Google Research with 170 million parameters, which can perform a variety of natural language processing tasks such as translation, summarization, Q&A, etc.

CLIP (Contrastive Language-Image Pre-training)

A cross-modal pre-training model proposed by OpenAI with 400 million parameters, capable of understanding both text and images, and realizing tasks such as image classification and image generation.

DALL-E

An image generation model developed by OpenAI with 1.2 trillion parameters, capable of generating images matching text descriptions.

Domestic large language model

Currently, China is also actively conducting research and development of large language model. Here are some Chinese Big Model projects:

Baidu - Wenxin Yiyan

Baidu's Wenxin Yiyan (ERNIE series) is a pre-trained language model with powerful natural language understanding and generation capabilities. Baidu uses the model to achieve intelligent upgrades and provide accurate and personalized services in scenarios such as search, information flow recommendation, advertisement placement, intelligent writing, and dialogue system.

It is widely used in search engine optimization, personalized news recommendation, automated advertisement content generation, assisted writing, and providing intelligent response to dialogue systems.

Alibaba-Tongyiqianqian

Alibaba's Tongyi Thousand Questions model is built based on Alibaba Cloud and is applicable to multiple business scenarios such as e-commerce, finance, logistics, etc. It optimizes product recommendation algorithms, improves customer service efficiency, assists in decision-making and analysis, and provides technical support for text generation, Q&A interaction, and other aspects.

It plays a role in e-commerce recommendation systems, intelligent customer service, logistics optimization, financial risk assessment and other business scenarios, and improves user experience through text generation and Q&A interaction.

Huawei-Pangu

Huawei's Pangu Big Model series aims to promote technological innovation in cloud computing, IoT, smart terminals, and other fields through deep learning technology. Pangu Big Models can be applied to Huawei cloud services, empower industry solutions, and provide intelligent functions for smartphones, smart homes, and other smart hardware devices.

Services are provided on Huawei's cloud platform to support functionality enhancement of smart terminal devices, such as smartphones and smart homes, as well as to provide deep learning models as a service (MLaaS) in industry solutions.

KU Xunfei-Starfire

Starfire Big Model launched by KU Xunfei is a cognitive intelligence large language model that integrates a variety of natural language processing and machine learning technologies, and is widely used in education, healthcare, government services, justice and other industry application scenarios, especially in intelligent speech synthesis, speech recognition, semantic understanding and knowledge graph construction.

In industries such as education, healthcare, government services and legal counseling, StarFire Big Model is able to provide voice interaction, automatic voice translation, intelligent voice assistant and other functions.

SenseNova - SenseNova

The "SenseNova" large language model system released by Q-Tech demonstrates AI model application capabilities such as Q&A, code generation, 2D/3D digital human generation, 3D scene/object generation, and so on. SenseNova's large language model show strong capabilities in the fields of professional text understanding, code generation, and assisting preliminary medical consultations.

SenseNova is able to provide innovative AI-driven solutions in areas requiring professional text analysis, code generation, digital human interaction and 3D scene rendering.

Tencent-Mixed Element

Tencent Hybrid Big Model is a utility-grade large language model self-researched by Tencent's entire chain, with a parameter scale of over 100 billion, which has been deeply applied to multiple business scenarios such as Tencent Cloud, Tencent Ads, Tencent Games, and Tencent Fintech.

In multiple business scenarios such as Tencent cloud services, advertising systems, game development, financial technology products, and social networks, the hybrid large language model is able to improve the level of service intelligence.

Baichuan Intelligence-Baichuan Series

Baichuan Intelligence released Baichuan series models, including Baichuan2-7B, Baichuan2-13B, etc., which are the first open-source large language model in China, with better performance in text capability, suitable for knowledge Q&A, text creation and other scenarios.

It is suitable for application scenarios that require tasks such as knowledge quiz, text creation, content analysis, etc. It is especially suitable for Chinese language environment.

Wisdom Spectrum AI-GLM Series

The GLM series large language model of Wisdom Spectrum AI, such as GLM-4, are bilingual large language model based on the GLM-130B, which is based on the Gigabit base model and has the functions of Q&A, multi-round dialog, and code generation. In domestic and international large language model evaluations, GLM-4 performs brilliantly and is close to the level of international first-class models.

In the internationalized application environment, GLM series large language model can provide multi-round dialogue management, solution generation for programming problems, and complex language translation services.

Development Direction and Trend

In the field of artificial intelligence, Large Language Models (LLMs) are becoming the core of technological innovation. With the continuous advancement of technology, the development direction of LLMs shows diversified trends, including multimodal fusion, self-supervised learning, augmented learning and self-regulation, decentralized learning, interpretability and transparency enhancement, lightweight design, domain customization, linguistic diversity, social responsibility and ethical norms, global cooperation, and ecological sustainability.

Multi-modal Integration

Future large language model will be more fused to handle multimodal data, such as text, image, video, etc., to achieve effective interaction and integration of different modal information. This fusion will further enhance the performance of models on multi-domain tasks. For example, multimodal models combining visual and linguistic information can provide more accurate diagnosis in medical image analysis or integrate visual perception with natural language commands in autonomous driving.

Self-Supervised Learning

Self-supervised learning will play an even more important role in the future of large language model as a key technique to reduce the reliance on labeled data. By automatically generating labels or tasks, models can be trained on large amounts of unlabeled data, improving performance while reducing the cost and time of data labeling. It is estimated that the global data volume will reach 175 ZB by 2025, which provides a huge data base for self-supervised learning.

Enhanced Learning and Self-Regulation

Large models with self-learning and self-regulation capabilities will be more flexible and efficient. Through augmented learning methods, models can self-improve based on environmental feedback, quickly adapt to new domains and tasks, and achieve continuous optimization and evolution. This capability is crucial for the rapidly changing Internet environment and the constantly updated technological demands.

Decentralization and Federated Learning

Data privacy and security considerations will drive large language model toward decentralization and federated learning. This model allows sharing and collaborative training of models across different data sources without centralizing the data, thus improving data privacy protection. By 2023, Gartner predicts that 90% of organizations will adopt federated learning to address data privacy and compliance concerns.

Interpretability and Transparency

Improving the interpretability and transparency of large language model is an important direction for future research. Users and regulators will be able to better understand the models' decision-making process, increasing trust in AI systems and promoting responsible AI deployment. With the implementation of regulations such as the EU General Data Protection Regulation (GDPR), model interpretability has become a legal requirement.

Lightweight and low-power design

With the proliferation of mobile devices and edge computing, future large language model will focus more on lightweight and low-power designs to enable them to operate effectively in resource-constrained environments. This will drive the development of model optimization techniques such as knowledge distillation and model pruning to reduce model size and computational requirements.

Domain-specific customized models

Increased demand for personalization and customization will drive large language model to be domain-specific, resulting in customized solutions for industries such as healthcare, legal, and finance to provide more accurate services. The healthcare AI market is expected to reach $36.1 billion by 2026, according to a report by MarketsandMarkets, demonstrating the huge potential of domain-specific models.

Linguistic diversity and cross-cultural understanding

In the context of globalization, large language model will focus more on linguistic diversity and cross-cultural understanding, processing data from different languages, dialects, and cultural backgrounds for broader cross-cultural applications. With the advancement of global cooperation projects such as "One Belt, One Road", cross-language and cross-cultural understanding will become increasingly important.

Social Responsibility and Ethics

The development of Big Models will place greater emphasis on social responsibility and ethical norms to ensure that the application of technology is ethical, avoids bias and discrimination, and promotes equity and inclusiveness. The promotion of Corporate Social Responsibility (CSR) and the Sustainable Development Goals (SDGs) requires that the development of AI technologies must consider their social impact.

Global Collaboration and Open Innovation

The future development of large language model will advocate global cooperation and open innovation to accelerate progress in the AI field by sharing data, knowledge, and technology for a win-win situation. The establishment of open source projects and international cooperation networks will promote AI research and application on a global scale.

Ecological Sustainability

As concerns about environmental impact increase, Big Model will focus on eco-sustainability, optimizing energy efficiency, reducing carbon footprints, and promoting environmentally friendly AI technologies. According to the United Nations Environment Program, digital technologies can help reduce global carbon emissions by 15%, with AI technologies playing a key role.