The advancement of artificial intelligence (AI) and machine learning (ML) has paved the way for businesses to make informed decisions, optimize their operations, and create innovative solutions. One critical component of AI and ML that has been pivotal in this revolution is large language models (LLMs).
Specifically, LLMs are machine learning models designed to understand, interpret, and generate human-like text. Due to their design, language models have become indispensable in various applications such as text generation, text summarization, text classification, and document processing. Given the benefits of these applications in the business world, we will now explore how large language models are built and how we at Multimodal can help.
How To Build Large Language Models
A large language model can be built by using three main approaches: (1) building a large language model from scratch, (2) fine-tuning existing pre-trained models, or (3) customizing existing pre-trained models.
Building A Large Language Model From Scratch
Building a large language model from scratch requires a comprehensive understanding of the underlying principles of machine learning and natural language processing (NLP).
- Machine learning knowledge is critical for selecting the right algorithms, training the model, and evaluating its performance.
- NLP knowledge is crucial for preprocessing text data, selecting relevant features, and understanding the linguistic nuances that the model needs to capture.
Both are integral to building a robust and effective language model.Let’s now look at the necessary steps involved in building an LLM from scratch.
1. Define Objectives
Start with a clear problem statement and well defined objectives. For example, “develop a highly accurate question-answering model with strong generalization abilities and evaluation on benchmark datasets”.
2. Data Collection
Next, collect a large amount of input data relevant to the task at hand. For an LLM, the data typically consists of text from various sources like books, websites, and articles. The quality and quantity of training data will directly impact model performance.
3. Data Preprocessing
Once the data is collected, it needs to be preprocessed to make it suitable for training the model. This involves cleaning the data by removing irrelevant information, handling missing data, and converting categorical data into numerical values.
Depending on the type of data you use, you may need to use additional preprocessing techniques, such as anonymization (necessary when using personal or sensitive information in datasets).
4. Model Selection
Different models specialize in different natural language processing (NLP) tasks. Selecting the one that’s right for your use case helps you achieve high performance with less training. For example, a regression model is used for tasks like predicting numerical values while a classification model is used for tasks like categorizing text in document processing.
5. Model Training
After selecting the appropriate model, the next step is to train it using the input data. This training process involves feeding all the input variables and training data into the model and adjusting the model's parameters to minimize the error between the predicted outputs and the actual output.
6. Model Evaluation
After the training is complete, the model's performance needs to be evaluated using a separate set of testing data. This involves comparing the model's predictions with the actual outputs from the test data and calculating various performance metrics such as accuracy, precision, and recall.
Recall can be defined as the ratio of the number of true positive predictions: the number of positive instances correctly identified by the model to the total number of actual positive instances in the dataset. It is particularly important in business situations like sentiment analysis for a new product, as the cost of false negatives is high and has spillover effects.
7. Model Tuning
Based on model evaluation, the model's parameters require to be fine-tuned for improved performance. We’ll discuss fine-tuning in more depth later, but note that it can nvolve adjusting for the learning rate, the number of layers in the model, or the number of neurons in each layer.
- The learning rate refers to a hyperparameter that determines the size of the steps that the model takes during the training process.A too small learning rate makes the model learn very slowly, while a large learning rate may make the model oscillate or overshoot the minimum.
- The number of layers in the model refers to the depth of the neural network. Adjusting the number of layers is essential because a model with too few layers may not be able to capture the complexity of the data, while a model with too many layers may overfit the training data and not generalize well to new, unseen data.
- The number of neurons in each layer refers to the width of each layer in the neural network. Adjusting the number of neurons is important as too few neurons may not be able to capture the underlying patterns of the data, while too many neurons may lead to overfitting and increased computational cost.
8. Model Deployment
Once the model is trained and fine-tuned, it is finally ready to be deployed in a real-world environment and make predictions on new data. It is also important to continuously monitor and evaluate the model post-deployment.
To do this effectively, implement a robust monitoring system that not only tracks the model's predictive performance but also its decision-making patterns. Utilize techniques such as A/B testing with different model versions to rigorously compare their performances and ensure that the deployed model is the most optimized version for your specific application.
Fine-Tuning Existing Pre-Trained Models
Using pre-trained models (PLMs) is another approach to building LLMs. A PLM is a machine learning model that has already been trained on a large dataset and can be fine-tuned for a specific task. This approach is often preferred as it saves a lot of time and resources required to train a model from scratch.
1. Select a Pre-Trained Model
There are various pre-trained model versions available for different tasks. Some popular pre-trained models for text generation are GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers). Selecting the type of PLM directly depends on the target task and objective.
2. Prepare Your Data
Even though you are using a pre-trained model, you still need to prepare your data for the specific task you are working on. This involves collecting relevant data, preprocessing it, and converting it into a format that can be fed into the model.
3. Fine-Tune the Model
Fine-tuning involves adjusting the model's parameters to make it more suitable for your specific task. As explained earlier above, this involves adjusting for the learning rate, the number of layers in the model, or the number of neurons in each layer.
💡Pro Tip: Before fine-tuning the PLM on the full data set, use a smaller subset of your data as it allows for quicker processing, feedback and corrective action that helps save time and preserve resources.
4. Evaluate the Model
After fine-tuning the model, it is essential to evaluate its performance on a testing dataset to ensure it is making accurate predictions and not overfitting.
5. Deploy the Model
Once the model is fine-tuned and evaluated, it can be deployed in a real-world environment to make predictions on new data.
Customizing Existing Pre-Trained Models
Customization is similar to fine-tuning in that it involves modifying an existing PLM to improve its performance on selected tasks or datasets. However, it typically takes less time and computational power because it requires updating only one or a few selected parameters of a PLM.Fine-tuning, on the other hand, requires modifying the entire model.
Besides being time-consuming, fine-tuning also yields a new model for each downstream task. This may decrease model interpretability, as well as the model’s performance on more diverse tasks compared to more basic and wide range function LLMs.
That’s why many data scientists and organizations are now turning towards customization and parameter-efficient fine-tuning (PEFT) techniques that involve optimizing model performance with minimal changes to existing parameters.
Determine the PEFT Technique:
Decide which parameter-efficient fine-tuning (PEFT) technique you will use based on the available resources and the desired level of customization.
Some examples of PEFT techniques are:
- Prompt Learning: It is a method where prompts, or special input modifications, are used to guide the model to produce desired outputs without extensively modifying the model parameters.
- Adapter Tuning: This involves inserting small, trainable modules (adapters) between the layers of a pre-trained model and only updating the parameters of these modules during training, leaving the original model parameters untouched.
- LoRA (Layer-wise Relevance Analysis): A technique used for interpreting the importance of each neuron in the model, which in turn helps in adjusting the model for better performance without a complete retraining.
- Reinforcement Learning from Human Feedback (RLHF): It is a technique where the model is fine-tuned based on feedback from human interactions rather than relying solely on the original training data. This helps the model to align better with human values and expectations.
1. Unfreeze the Model Layers Gradually
It is important to gradually unfreeze or rework the layers of the model, starting from the last layer, to avoid losing the knowledge gained during pre-training. This process helps in retaining the original model's capability while adapting to new data.
2. Regularize Learning
Try for the weights of the updated model to stay close to the initial weights. This ensures that the model does not diverge too far from its original training which regularizes the learning process and helps to avoid overfitting.
3. Adjust the Learning Rate
Assign a lower learning rate to the bottom layers of the model. This ensures the foundational knowledge of the model is not drastically altered, while still allowing for necessary adjustments to improve performance.
4. Train the Model
Use your prepared data to train the model. Monitor its performance throughout the training process to ensure it is learning and improving.
5. Evaluate the Model
After training, evaluate the model's performance using a separate testing dataset that it has not seen before. This will give you an idea of how well the model will perform in a real-world scenario.
6. Deploy the Model
Once you are satisfied with the model's performance, it can be deployed for use in your application.
Remember, customization is essential for aligning the model with specific business requirements and for improving performance on the unique tasks your organization faces. Following these steps will help ensure that your model is effectively customized while retaining the valuable knowledge it has already gained during pre-training.
The Pros & Cons: Building From Scratch VS Fine-Tuning VS Customization
- Building from Scratch: Requires a profound understanding of machine learning algorithms, data preprocessing techniques, and model evaluation methods.
- Fine Tuning: Demands less expertise as the model is already trained and only needs adjustment for the specific task.
- Customization: Strikes a balance between the two, requiring some expertise to modify the model according to the task while leveraging the pre-trained model's existing knowledge.
🎯 The Winner: Customization. It leverages existing knowledge while still allowing for task-specific adjustments, providing a balance between expertise requirements and efficiency.
- Building from Scratch: Allows you to customize the model's architecture to suit your specific needs, beneficial for unique requirements not met by existing pre-trained models.
- Fine Tuning: Offers less flexibility as you are limited to the architecture of the pre-trained model.
- Customization: Provides some flexibility in modifying the model while still utilizing the base architecture of the pre-trained model.
🎯 The Winner: Building from Scratch. It provides the highest flexibility by allowing complete customization of the model's architecture to suit specific needs.
- Building from Scratch: Requires a large amount of training data and computational resources, making it time-consuming and expensive.
- Fine Tuning: Requires less training data and computational resources as the model is already trained on a large dataset.
- Customization: Requires moderate resources as it leverages the pre-trained model but still necessitates some training data and computational resources for the task-specific adjustments.
🎯 The Winner: Fine Tuning. It is the most resource-efficient option as it leverages an already trained model, requiring less training data and computational resources.
- Building from Scratch: May result in lower performance compared to fine-tuning a pre-trained model as it has to learn all features from the ground up.
- Fine Tuning: Often results in better performance as the pre-trained model has already learned useful features from a large dataset.
- Customization: Performance may vary, but it often results in good performance as it leverages the existing knowledge of the pre-trained model while allowing for task-specific adjustments.
🎯 The Winner: Customization. Although performance may vary depending on the task and data, customization generally provides good performance by balancing the benefits of the pre-trained model and task-specific adjustments.
In summary, building a model from scratch offers the highest degree of flexibility but it also requires the most expertise, data, and computational resources whereas fine-tuning a pre-trained model is the most resource-efficient method but offers less flexibility.
On the other side, customization strikes a balance between flexibility, resource intensity, and performance, potentially offering the best of both worlds. Therefore, customization is often the most practical approach for many applications, although the best method ultimately depends on the specific requirements of the task.
Considerations Before Getting Started
Before you start building your custom LLM, it is essential to consider the following:
- Understand Your Data: It is crucial to have a thorough understanding of your data, including its characteristics, limitations, and potential biases. This will help you select the most appropriate model and preprocessing techniques for your task.
- Define Your Objectives: Clearly define your business objectives and how the model will help achieve them. This will help you select the most appropriate model and evaluation metrics for your task.
- Consider Your Resources: Building and deploying a machine learning model requires computational resources, time, and expertise. It is essential to consider the resources required for training different model versions and select an approach that is feasible for your organization.
- Evaluate Different Approaches: It is essential to evaluate different approaches and select the one that offers the best trade-off between performance, complexity, and resource requirements.
Need Help Building Your Custom LLM? Let’s Talk
If building a large language model seems like too challenging a task to handle on your own, get in touch with our AI experts. We specialize in building Custom Generative AI for organizations, and can deliver projects in less than 3 months.
Schedule your free, non-obligatory meeting today.