Training Large Language Models: Simply explained

What are Large Language Models (LLMs)?

LLMs are the backbone of various generative AI applications. The models are trained on massive amounts of text data and can understand, interpret, and generate human language. Some of the common LLMs include BERT, ChatGPT, and Llama. Please refer to Introduction to Large Language Models for a detailed understanding of LLMs architecture and Use cases of Large Language Models to understand the value LLMs provide to various businesses.

Three Training Phases of Large Language Models

Training an LLM is a multi-layered process, with each process having a unique role and contribution to the model's performance. In this section, we provide a detailed description of self-supervised, supervised, and reinforcement learning, as they play crucial roles in making LLMs capable of generating outputs that power various business applications. It is essential to note that though each training phase has its unique role, the collective role of the three phases results in an effective and well-functioning LLM.

Following are the three main training phases of an LLM:

1. Self-Supervised learning: The first training stage involves feeding the model with massive amounts of raw data and having it predict missing pieces of it. Through this process, the model learns something about language and the domain of data to generate probable answers. The main focus of self-supervised learning is on predicting words and sentences.

2. Supervised learning: Supervised learning is the second stage in training LLMs and is the crucial phase that builds upon the foundational knowledge acquired during the self-supervised learning stage. In this phase, the model is exclusively trained to follow instructions and learns to respond to specific requests. The model becomes more interactive and functional during this stage. This stage prepares the model to interact with users, understand their requests, and provide valuable responses.

3. Reinforcement learning: This is the final stage in the training process. It encourages desired behaviours and discourages undesirable outputs. It's a unique stage as the model isn't provided with exact outputs to produce; instead it grades the output it generates. The process begins with a model capable of following instructions and predicting language patterns. This follows data scientists using human annotations to distinguish between good and bad outputs. These annotations act as guidelines for the model and facilitate its understanding of preferred and nonpreferred responses. The feedback gathered from the annotations is used to train a reward model. The reward model is critical as it guides the model towards producing more desirable responses and discouraging less desirable ones. This method is especially advantageous in discouraging harmful and offensive language and encouraging quality responses from the language model.

When Should You Start Or Give Up Your Own Training?

When companies should train LLMs with their own data

Evaluating the process and feasibility of fine-tuning or domain adaptation for specific use cases can help decide whether or not a company should train LLMs with their own data. Fine-tuning is a technique that helps train a general-purpose, pre-trained model for a specific application. For instance, you can fine-tune BERT to answer customer queries for your marketing agency. On the other hand, domain adaptation helps further train an LLM to understand a domain-specific language. For instance, domain adaptation might help the model understand medical, legal, and technical jargon.

Hence, if you discover that the prediction quality of the existing models doesn't adequately capture your use case or if your documents use domain-specific language that the existing domain-specific models such as LEGAL-BERT or SciBERT are unable to represent, then it's best to utilise data annotation and subject pre-trained models to a few more training steps.

When companies should not train LLMs with their own data

A company should refrain from training its own transformer-based language models from scratch as the process is time and resource-intensive. The process can take weeks or months. It requires extensive resources such as GPUs, CPUs, RAM, storage, and networking. You could have ample time and resources to train LLMs, but you'd also need commensurate human expertise to bring your vision to life. Human expertise needs specialisation in ML and NLP. Your training data needs to be massive and clean. Lastly, LLMs require high maintenance. Hence, you should consider these factors before training LLMs with your own data.

Added value of proprietary and open models

Even without training, you can leverage the power of LLMs using proprietary or open-source models due to the following reasons:

Proprietary models developed by companies such as OpenAI and Google are already trained on large amounts of data and can handle various tasks. You can subscribe to the service and scale LLM usage based on needs. This facility lets your business focus on its core competencies while leveraging pre-built LLMs.

Open-source models allow for customisation by fine-tuning your company's specific data, resulting in a more tailored solution for your business needs. Choosing open-source models means that you can work with continuously improved LLMs as a large developer community works on troubleshooting and enhancing future versions.

Both proprietary and open-source models provide value for companies, even without in-house training. Choosing between the two depends on the company's needs, resources, and data security requirements.

Challenges During Training

Following is a tabular description of challenges an organisation could potentially face while implementing LLM training:

Step-by-step Guide for Training LLMs

The following guide provides a high-level description for training LLMs:

1. Defining business objectives: You must know what you want the LLM to achieve. For instance, LLMs are successfully deployed for language translation, question answering, content generation, etc. Choosing your use case based on business objectives will help guide your decisions throughout the process.

2. Acquiring and processing data: Successful LLM implementation depends on the quality of the data on which it is trained. Therefore, it's a huge responsibility to gather data that aligns with business objectives and the application and is free from bias and errors. This stage also involves removing irrelevant information from the data and formatting it correctly. This step may include tokenisation, normalisation, and data augmentation.

3. Choosing a pre-trained model or architecture: Next, you would need to select a pre-trained LLM architecture that suits your business objectives. Some examples include GPT, BERT, and T-5. You should decide whether you want to use a publicly available pre-trained model, such as those available from Hugging Face or Google AI, or a custom architecture.

4. Setting up your training environment: This stage includes acquiring the necessary hardware, such as powerful GPUs or specialised AI accelerators, and software tools, such as deep learning frameworks like TensorFlow or PyTorch.

5. Hyperparameter tuning: Hyperparameters are settings within the LLM architecture that influence its training process. Some examples include batch size and learning rate. Finding the optimal hyperparameter configuration for your specific objectives requires experimentation.

6. Training the model: In this stage, the LLM learns from the data. The model iteratively processes the data and adjusts its internal parameters to improve its ability to predict the next word or generate human-like text. The process is time-consuming and can take days or months, depending on model size and complexity.

7. Evaluating and monitoring: It's essential to continuously assess LLMs performance on a separate dataset which wasn't used for training. Measure metrics specific to your task, such as accuracy, BLEU score (for translation tasks), or ROGUE score (for summarisation). Identify potential issues regarding errors through techniques like logging and visualisation.

8. Fine-tuning: This is an optional step if your business objectives are specific. In such cases, you can fine-tune the pre-trained LLM on a smaller dataset tailored to your domain. The process helps the model adapt to your particular use case with improved performance.

9. Deployment: Once the LLM performance is satisfactory, it's ready for integration into your desired application or service. This might involve setting up APIs to allow other programs to interact with your LLM.

10. Maintenance and improvement: It's necessary to stay updated with the latest advancements in the field and consider retraining your model with new data or improved techniques to maintain and enhance its effectiveness.

Conclusion

LLMs have proven to be a valuable asset for businesses across various domains. The decision to proceed with training should be based on the ability of existing models to capture the use case adequately and the availability of resources and expertise required for the training process. Ultimately, a thoughtful approach to training and fine-tuning LLMs can lead to the development of highly effective and impactful language models for business applications.