How Are Large Language Models (LLMs) Trained? A Deep Dive into AI Learning
Even if you haven’t heard of the term “Large Language Model,” you’ve probably heard of some LLM products; ChatGPT and DeepSeek are some of the most popular LLMs available on the market today. Large Language Models (LLMs) are a type of AI that can generate text and even emulate human language based on context from written or even spoken prompts. They have several uses that make them invaluable to organizations all over the world, such as data summarization, report generation, idea creation, and much more. In order to achieve this level of capability, LLMs need to be trained to generate the results that you want, and the process for doing so requires large amounts of GPUs and TPUs to accomplish. However, it’s still important for IT leaders to understand how this process works on a high level. Let’s review the steps needed to effectively train a Large Language Model and why each step is important to creating a useful AI application.
Collect Data and Create Datasets
LLMs require vast amounts of data to perform a desired task, so the data collection step is crucial to the success of the LLM. This data needs to be converted into usable datasets for the LLM to reference, and the text must have corresponding sources to ensure accuracy. Generally speaking, the more data that is fed to an LLM, the more accurate its answers and responses will be.
Set Up The Transformer Neural Network
The transformer neural network is the architecture that defines how an LLM processes data; it’s like a massive network of synthetic neurons that determines a LLM’s operational capabilities. An important part of this process is tokenization, which involves turning large datasets into smaller, encoded units that are easier for the LLM to understand. These “tokens” are essential to forming the foundation for LLMs, as the transformer neural network can more easily handle these pieces of information to accomplish NLP tasks.
Train the LLM
Now that the LLM has its datasets and parameters in place, it’s finally time to begin training the system. As mentioned previously, training an LLM requires vast amounts of processing power to complete; not only do they have billions of parameters that need to be processed, but they also need to be fine-tuned, which requires even more processing power. However, there are some training methods that require human involvement. One effective training method is creating a system where the LLM adjusts its responses to queries based on human feedback. Doing this allows for more accurate responses and eliminates the possibility of grammatical, factual, and other types of errors.
Fine-Tune the Model and Make Adjustments
The process for configuring a LLM does not end with the training process; it’s crucial to conduct a series of tests on the model to fine-tune its capabilities. Creating a reward model–a bit of code that can label data as ideal or not ideal–can further fine-tune the LLM at a faster pace than human testers. The fine-tuning process is also an ideal time to input highly-specific information that the LLM could use to create more realistic results. For example, if an LLM is being fine-tuned to instantly solve mathematical problems, it can be fine-tuned with knowledge of specialized equations to help it solve a wider variety of problems.
Explore the Possibilities of LLMs with CCG!
LLMs have tremendous potential in the world of business, but there are a lot of options available for IT leaders to choose from. At CCG, our team can work with you to evaluate your business’s short and long-term needs and find an LLM application that addresses your goals. We’re partners with several of the industry’s top IT providers, and we’re committed to finding solutions that can create an immediate impact on your operations. Ready to join the AI revolutions? Start a conversation with our team today to get started!