Training Generative AI: Methods for Model Training

What is training a generative AI model?

Training a generative AI model is the process of feeding it large amounts of data and having it learn patterns and features from that data so that it can generate high-quality content that looks like it was made by a human.

Basic knowledge of generative AI models

Model types and features

There are various types of generative AI models. The three most common are:

1. Large-scale language models (LLM): They can learn from huge amounts of text data and generate natural, human-like sentences. ChatGPT is a typical example.
2. Image generation models: These models can learn large amounts of image data and generate images in a variety of styles, including photographs, illustrations, and paintings. Stable Diffusion and Midjourney are some of the most well-known models.
3. Speech generation model: It can learn speech data and generate human voices, music, sound effects, etc. It is used in speech synthesis technology and music production tools.

Each of these models has different strengths in terms of tasks and features. For example, LLM is good at text generation and translation, but not at image generation. On the other hand, image generation models can generate high-quality images, but cannot generate text.

Model training data

The performance of a generative AI model depends heavily on the quality and quantity of training data. Training data is the data that serves as the teaching material for the model to learn. For example, a text generation AI uses a large amount of text data (books, articles, websites, etc.) as training data.

The better the quality of the training data, the more accurate and natural the content the model can generate, and the more training data there is, the more diverse the patterns the model can learn and the more versatile the content it can generate.

The purpose and importance of training

Training a generative AI model serves three purposes:

1. High-quality content generation: Leverages knowledge gained from training data to generate high-quality content that looks like it was created by a human.
2. Task-specific specialization: Train models that specialize for specific tasks (e.g. translation, summarization, image generation, etc.).
3. Improve and evolve your models: Improve your models’ performance to handle more advanced tasks.

Training generative AI models is crucial to achieving these goals: proper training can maximize the model’s performance and generate more valuable content.

Training Process

Training a generative AI model typically involves the following steps:

1. Data collection and preprocessing: First, a large amount of data is collected for the model to learn. The type of data varies depending on the type of model. For example, text data is collected for a text generation AI, and image data is collected for an image generation AI. The collected data is preprocessed to remove noise and unnecessary information.
2. Model construction and training: Next, a model is constructed based on the collected data. Various algorithms and architectures are used to construct the model. Training data is input into the constructed model, and training proceeds while adjusting parameters.
3. Model evaluation and tuning: We evaluate the trained model and adjust the parameters as necessary. For evaluation, we use test data to verify the quality and accuracy of the content generated by the model. Based on the evaluation results, we may improve the model structure and training method and retrain the model.

By repeating these steps, the model’s performance is gradually improved. Training requires time and computational resources, but it is a necessary process for developing high-quality generative AI models.

Primary Training Methods

There are various methods for training generative AI models. Here we introduce three representative methods.

Supervised learning

Supervised learning is a method of pairing input data with its correct label (teacher data) and having the model learn it. For example, in the case of image generation AI, by pairing image data with its corresponding label (such as “dog”, “cat”, “car”, etc.) and having the model learn it, the content of the image can be recognized.

• The role of training data: Training data is a guide for the model to learn. The more training data there is, the more patterns the model can learn and the more accurate it can be.
• Advantages and disadvantages of supervised learning: The advantage is that if there is a lot of training data, a highly accurate model can be constructed. The disadvantage is that it takes time to create training data.

Unsupervised learning

Unsupervised learning is a method in which a model discovers patterns and features by itself from data that does not have correct labels. For example, by training a model on a large amount of text data, it can understand the meaning of words and grammatical structures.

• Unsupervised learning algorithms: There are many different algorithms in unsupervised learning, including clustering, dimensionality reduction, and anomaly detection.
• Advantages and disadvantages of unsupervised learning: The advantage is that there is no need to create training data. The disadvantage is that the accuracy of the model tends to be lower than that of supervised learning.

Reinforcement learning

Reinforcement learning is a method of training a model through trial and error. The model receives rewards or penalties for taking certain actions, and improves its behavior based on those rewards. For example, in the case of a game AI, rewards are given for scoring while playing a game, and penalties are given for making mistakes.

• Rewards and Penalties: Rewards and penalties are key factors for a model to learn. Setting appropriate rewards and penalties can help the model choose better actions.
• Applications of reinforcement learning: Reinforcement learning is applied to a variety of fields, including not only game AI but also robot control and autonomous driving.

A caveat to training generative AI models

Training generative AI models is very important for generating high-quality content, but there are some things to be careful about. Here, we will explain from four perspectives: data quality and quantity, tuning hyperparameters, overfitting and underfitting, and ethical considerations.

Data quality and quantity

The performance of a generative AI model depends heavily on the quality and quantity of training data. High-quality data is data that is highly relevant to the task the model wants to learn and has little noise or errors. For example, when training a text generation AI, text data that is grammatically correct and contains a variety of expressions is required.

The amount of data affects the diversity of patterns a model can learn. In general, the more data you have, the more complex the patterns a model can learn and generate more versatile content. However, simply increasing the amount of data does not necessarily improve the model’s performance. Conversely, the performance of a model may decrease if the data is of poor quality.

How we collect data

There are two main ways to collect data:

• Use of public data: There are various types of public data on the Internet, such as Wikipedia articles, news site articles, SNS posts, etc. By utilizing this public data, it is relatively easy to collect large amounts of data.
• Creating your own data: It may be difficult to train a model specialized for a specific task using only public data. In such cases, you need to create your own data. For example, if you want to generate sentences that contain technical terms from a specific field, you need to ask experts in that field to help you create sentence data.

Cleaning and preprocessing data

The data collected may not be suitable for training a model as is, so data cleaning and preprocessing are required.

• Cleaning: Remove noise from your data (typos, duplicate data, profanity, etc.).
• Preprocessing: Converting data into a format that is easy for a model to learn from, for example converting text data into numerical data or resizing image data to a specific size.

Data cleaning and pre-processing must be done carefully as it has a significant impact on model performance.

Hyperparameter tuning

Hyperparameters are parameters that are set by humans during the model training process. Typical hyperparameters include the learning rate, batch size, and number of epochs.

What are hyperparameters?

• Learning rate: A parameter that adjusts how much the model learns at one time. If the learning rate is too large, the model may not converge to the optimal solution and learning may become unstable. Conversely, if the learning rate is too small, learning may take too long.
• Batch size: A parameter that adjusts the number of data used in one training. If the batch size is too large, it may consume a lot of computing resources and take a long time to train. Conversely, if the batch size is too small, training may become unstable.
• Number of epochs: This parameter adjusts how many times the training data is trained. If the number of epochs is too large, overfitting may occur, and accuracy may decrease for unknown data. Conversely, if the number of epochs is too small, the model may not be able to learn sufficiently, resulting in low accuracy.

Adjustment method and precautions

Tuning hyperparameters is a trial-and-error process. Generally, methods such as grid search and random search are used to find optimal hyperparameters. However, tuning hyperparameters must be done carefully because it has a significant effect on the model’s performance.

Overfitting and Underfitting

When training generative AI models, problems of overfitting and underfitting can occur.

• Overfitting: Overfitting is a phenomenon in which a model becomes overly adapted to the training data and is unable to respond well to unknown data. For example, a text generation AI that has only been trained on text data written in a particular style may have difficulty generating text in other styles.
• Undertraining: Undertraining is when a model cannot fully capture the features of the training data and cannot generate appropriate content. For example, this is likely to occur when there is too little training data or the data quality is poor.

Causes of overfitting and underfitting

• Overfitting: This can occur due to too little training data, a model that is too complex, or training time that is too long.
• Undertraining: This can occur due to too little training data, too little training time, or a model that is too simple.

Countermeasure

• Overfitting: There are various countermeasures such as increasing the amount of training data, simplifying the model, introducing regularization (a technique to suppress overfitting), and early stopping (stopping training midway).
• Untrained: Countermeasures include increasing the amount of training data, lengthening the training time, and making the model more complex.

There is a trade-off between overfitting and underfitting: trying to improve one can make the other worse, so finding the right balance is important.

Ethical considerations

Ethical considerations are essential in the development and use of generative AI.

• Bias: Generative AI may reflect biases contained in the training data. For example, AI trained with data that contains prejudice against a particular gender or race may generate discriminatory content. To eliminate bias, it is necessary to train with diverse data and develop technology to detect and correct bias.
• Privacy: When generative AI collects and uses personal information, it must be mindful of privacy protection. It is important to comply with laws and regulations such as the Personal Information Protection Act and to collect and use data only with the user’s consent.
• Preventing misuse: Generative AI can also be used for malicious purposes, such as to create fake news or deep fakes. To prevent misuse, it is necessary to properly label the output of generative AI and develop technology to detect malicious use.

The ethical issues surrounding generative AI need to be discussed throughout society, rather than just be solved technically, and rules and regulations need to be established.

Summary: Training generative AI models is an investment in the future

Training a generative AI model is a key process for generating high-quality content and automating various tasks. Many factors affect the model’s performance, including proper data collection and pre-processing, choosing the right training method, and tuning hyperparameters.

Training a generative AI model is no easy task. However, it can also be considered an investment in the future. Developing a high-quality generative AI model can contribute to improving business efficiency, creating new services, and even solving social issues.

By overcoming ethical and technical challenges and further developing techniques for training generative AI models, we can build a richer, more creative future.