Navigating LLM Training: A Comprehensive Guide

A large language model (LLM) is a predictive foundation model trained on enormous stores of data to understand and generate information in a human-like way—that is, to learn from its mistakes via a system called deep learning. 

LLMs have a variety of use cases. These include: 

• Generating text

• Translating text

• Summarizing data

• Writing code

LLMs don’t learn on their own initially, however. You must train them properly. Understanding LLM training is vital to exploring further realms of machine learning and artificial intelligence (AI), particularly generative AI. 

Key components of LLM training

LLMs work via highly sophisticated predictive algorithms. In other words, they aren’t “intelligent” the way people are. In fact, in purely human terms, what they’re doing isn’t exactly “learning” at all. But by training your LLM carefully, you can get it to do something closer to learning than any machine has been capable of before.

Many vital components of LLM training are critical to developing a robust and versatile LLM. Major considerations include:

1. Data preparation and quality

• Source diverse and representative data: In order to get the most out of your LLM, ensure that you’re training it on diverse data sets representative of various languages, dialects, and demographics. This will help you prevent biases during training and improve your model's generalizability. Remember, good training is key from the start: An MIT study found that as much as 50 percent of LLM training data sets LLMs had errors [1]. 

• Pay attention to data cleaning: AI “hallucinations” continue to be troublesome. A hallucination is either a mistake or a lie, depending on how you want to think about it, and proper LLM data cleaning involves overseeing your LLM training to make sure it isn’t making things up, plagiarizing rather than learning, or going wildly off topic when delivering answers to prompts.

2. Model architecture and selection

• Choose an appropriate model size: The bigger the LLM model size, the more sophisticated the operations it can run. However, not all use cases call for huge LLMs. You will want to consider that your LLM needs to be downloadable—that is, not so big that your processor will crash if you try to install it on your computer—as well as functional to just the extent required. Too much functionality is a waste. 

• Make the right architecture choices: You can save time and money when developing an LLM training strategy by using open-source foundation models instead of creating bespoke architectural models. 

3. Regularization and generalization

• Avoid overfitting: Overfitting is the phenomenon by which an LLM algorithm can accurately predict and reproduce the data you trained it on, but can’t generate new data—in other words, it can memorize but can’t learn. To fix this problem, you’ll want to implement regularization, a penalty applied to an LLM when it doesn’t produce new data sets or settles for reproducing data noise—discrepancies in data collection.

• Try early stopping: You can also help eliminate the possibility of an LLM acquiring too much noise via early stopping. Early stopping is a manual way of shutting down an LLM’s training when you notice it learning noise or producing faulty new data. 

4. Efficient training techniques

• Use batch training and normalization: Batch training is training an LLM on a mass of data simultaneously rather than doing so piecemeal as new data comes in. This is a more accurate training method because new information may be faulty or heterogeneous—in other words, it may be just noise. This results in training normalization, which increases efficiency by standardizing the types of data you train your LLM on.

• Implement transfer learning: Transfer learning is having an LLM learn from smaller sections of its own current data set. This is an efficient way to train an LLM in the sense that it is essentially training itself. For instance, if your LLM learned to identify people, it can train itself to understand that a subset of those people—the small ones—are children. 

5. Monitoring and evaluation

• Continuously monitor the training process: You will want to monitor your LLM training process for mistakes. Tools and dashboards such as TensorBoard help make this a less arduous and more visually intuitive task. 

• Maintain security: According to a Gartner study, 60 percent of organizations that have adopted AI have experienced data compromise or similar security concerns [2]. You can mitigate security concerns by using only the amount of data you need, encrypting it, and making sure only those who need it have access to it.

6. Ethical considerations

• Mitigate bias: LLMs can pick up and learn from imperfect sources because they’re trained on massive data sets. Some of those sources display bias—including racism, sexism, and xenophobia. You may need to personally oversee LLM training and weed out biased sources individually. Once an LLM model is bias-free, you can train it to continue to be so via transfer learning.

• Observe transparency and explainability: Transparent LLM training methods allow stakeholders to understand where data comes from, assuring them that you didn’t make up or purposely skew data. Transparency also helps when you must explain why a training model didn’t work as well as it should have or why noise and bias found their way into your LLM. If there’s uncertainty about why these things happened, being transparent about your failures signals that you’re interested in building the most ethical training model possible. 

How to start with LLM training

Here's a structured path to help you begin your LLM training journey.

1. Understand the basics of AI and machine learning.

Explore the foundations of AI and machine learning on Coursera. Start with IBM’s Introduction to Artificial Intelligence (AI) course. This course can help you learn the core concepts of AI and how it affects business decisions. 

2. Dive deeper into natural language processing (NLP) and other concepts.

Once you’ve grasped the fundamentals, delve into more specialized topics such as natural language processing (NLP). NLP is the technology by which digital technology understands and responds similarly to human communication. 

Machine learning occurs via neural networks, which imitate the structure of the human brain. In a human-like way, these neural networks allow AI models to learn from their mistakes and adapt that knowledge to newer, unfamiliar knowledge. 

The most frequently utilized generative AI models use transformer architecture. Transformer architecture is a language algorithm by which an LLM predicts the next word in a sentence based on the probability that it will be correct, thereby producing human-like communication word by word. You can think of it as a highly sophisticated form of autocorrect. 

3. Research computational resources.

LLM training requires significant computational power, often involving a graphics processing unit (GPU), a software that allows generative AI to create images. LLM training may even demand more specialized hardware, such as a tensor processing unit (TPU). A TPU helps AI scale in a more cost-efficient way. 

To optimize and share your LLM model as widely as possible, you will want to use cloud-based infrastructure resources. Popular choices include: 

• Google Cloud (GCP)

• Amazon Web Services (AWS) 

• Microsoft Azure

Getting started in LLM training with Coursera

LLM training is a constant learning and adaptation journey for both you and your LLM. By embracing the complexities inherent in LLM training and continually refining your approach to it, you may contribute to the rapidly evolving field of AI.

Explore more on Coursera today. For example, AWS and Finetuning.AI teamed up to offer Generative AI with Large Language Models. From there, IBM’s Generative AI LLMs: Architecture and Data Preparation can take you further into practical LLM usage.