Large Language Models (LLMs for short) offer companies great added value in many areas. To keep up with the progress, it is important to know how the training of a large language model works and when an organisation should focus on training an LLM on its own with its data or fine-tuning an existing large language model. If you decide to use LLMs for your organisation, you need to understand the challenges that can arise. Whether you are training an LLM or using an existing one, you need to be aware of the training process as it gives you the opportunity to scrutinise its results before you deploy the model on a larger scale. This blog post will break down these complexities and give you the opportunity to make informed decisions.

What are Large Language Models?

Large Language Models (large language models, or LLMs for short) are the backbone of various generative AI applications. The Models are trained on large amounts of text data and can understand, interpret and generate human language. The most common LLMs include BERT, ChatGPT and Llama. Please read Introduction to Large Language Models for a detailed understanding of the architecture of LLMs and use cases of Large Language Models to understand the value LLMs offer to different organisations. 

Thanks to their human-like text generation, large language models improve technological efficiency in companies and are used in a wide range of applications in the business world.

An Introduction to Large Language Models

The 3 training phases of large language models 

Training a Large Language Model is a multi-layered process. In this section, we provide a detailed description of self-supervised, supervised and reinforcement learning, as they play a crucial role in enabling LLMs to produce results that support various business applications. It is important to note that although each training phase has its own role, the collective role of the three phases leads to an effective and well-functioning LLM.   

1. Self-Supervised LearningIn the first training phase, the model is fed with huge amounts of raw data and has missing parts of it predicted. Through this process, the model learns about the language and the domain of the data to generate probable answers. The main focus of self-supervised learning is on the prediction of words and sentences. 

• Supervised Learning: Supervised Learning (supervised learning) is the second stage in the training of Large Language Models and is the crucial phase that builds on the foundational knowledge acquired in the self-supervised learning phase. In this phase, the model is trained exclusively to follow instructions and learns to respond to specific requests. The model becomes more interactive and functional in this phase. In this phase, the model is prepared to interact with users, understand their requests and provide valuable answers. 

• Reinforcement LearningThis is the final stage of the training process. Here, desirable behaviour is encouraged and undesirable results are prevented. The model is not given exact results, but evaluates the results it produces. The process begins with a model that is able to follow instructions and predict speech patterns. Data scientists then use human annotations to distinguish between good and bad results. These annotations guide the model and help it understand preferred and non-preferred responses. The feedback gained from the annotations is used to train a reward model. The reward model is crucial as it guides the model to produce more desirable responses and suppress less desirable ones. This method is particularly beneficial when it comes to suppressing harmful and offensive language and encouraging high quality responses from the language model. 

For a compact introduction to the definition and terminology behind reinforcement learning, read our basic article on the methodology:

Reinforcement Learning: compactly explained

When does it make sense to train your own LLM?

Training of an LLM on your own database

The evaluation of the process and the Feasibility of fine-tuning or domain customisation for specific use cases can help in deciding whether or not a company should train large language models with its own data. Fine-tuning is a technique that helps to train a general, pre-trained model for a specific application. On the other hand, domain adaptation helps to further train an LLM to understand a domain-specific language. For example, domain customisation can help the model to understand medical, legal and technical jargon. 

So if you notice that the prediction quality of the existing models does not adequately capture your use case or if your documents use a domain-specific language. that the existing domain-specific models such as LEGAL-BERT or SciBERT cannot represent, then it is best to use data annotation and subject the pre-trained models to a few more training steps. 

Proprietary and open source models

A company should carefully consider whether it wants to use its own transformer-based language models would like to train from scratch, as this Process extremely time-consuming and resource-intensive is. The training process can take weeks or even months and requires extensive resources such as GPUs, CPUs, RAM, storage and networks. Even if a company has sufficient time and resources to train Large Language Models (LLMs), the training process can take weeks and even months, it also requires the appropriate human expertiseespecially in the areas of Machine learning (ML) and Natural Language Processing (NLP) in order to successfully realise the company's vision. In addition, the Comprehensive and well-prepared training data to develop effective models. Last but not least, the care and maintenance of LLMs requires considerable effort, which is why companies should carefully consider these factors before embarking on the path of in-house model training.

Offer an alternative to your own model training Proprietary modelsdeveloped by companies such as OpenAI and Google. These models are already trained on large amounts of data and can handle a variety of tasks. Companies have the option of utilising these services and scaling the use of LLMs as required. This allows them to focus on their core competences while reaping the benefits of ready-made LLMs without having to go through the complex and resource-intensive training process themselves.

In addition to proprietary models, there are also Open source modelsthat enable customisation by fine-tuning with a company's specific data. This option leads to customised solutions that are better suited to individual business requirements. Open source models benefit from a large developer community that continuously works on improving and debugging these models, which constantly increases the quality and functionality of these LLMs.

Overall, both proprietary and open source models offer considerable added value for companies, even without in-house training. The choice between these options depends on the company's specific needs, available resources and data security requirements. It is crucial to carefully weigh up the pros and cons of both models in order to find the optimal solution for your own organisation.

Large language models are transforming interaction with technology and expanding its application from content creation to customer service. Our overview presents 14 relevant representatives in detail:

The 14 Top Large Language Models: A Comprehensive Guide

Challenges of large language model training

The following is a tabular description of the challenges that a company faces when training large language models: 

Learn how Explainable AI (XAI) makes the decision logic of highly complex AI models such as Large Language Models (LLMs) understandable and trustworthy.

LLM Explainability: Why the "why" is so important

Model training: procedure and process

The following example provides an initial overview of the training of large language models: 

1. Definition of corporate goalsYou need to know what you want to achieve with the Large Language Model. For example, LLMs are successfully used for language translation, question answering, content creation, etc. Choosing the use case based on the business goals will help you make your decisions throughout the process. 
2. Collecting and processing data: A successful LLM implementation depends on the quality of the Training data on which it is trained. Therefore, it is a big responsibility to collect data that is in line with the business objectives and the application and free from bias and errors. At this stage, irrelevant information must also be removed from the data and the data must be formatted correctly. This step may include tokenisation, normalisation and data augmentation. 
3. Selection of a pre-trained model or architectureNext, you need to select a pre-trained architecture that matches your business goals. Some examples are GPT, BERT and T-5. You should decide whether you want to use a publicly available pre-trained model, such as the one from Hugging Face or Google AI, or a custom architecture. 
4. Setting up your training environmentThis phase includes the procurement of the necessary hardware, such as powerful graphics processors or specialised AI accelerators, and software tools, such as Deep learning frameworks such as TensorFlow or PyTorch.
5. Tuning the hyperparametersHyperparameters are settings within the architecture that influence the training process. Some examples are the stack size and the learning rate. To find the optimal hyperparameter configuration for your specific goals, you need to experiment.
6. Training of the modelIn this phase, the language model 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. This process is time-consuming and can take days or months depending on the size and complexity of the model. 
7. Evaluation and monitoringIt is important to continuously evaluate the performance of Large Language Models on a separate data set that was not used for training. Measure task-specific metrics such as accuracy, BLEU score (for translation tasks) or ROGUE score (for summaries). Identify potential error issues through techniques such as logging and visualisation. 
8. Fine tuningThis is an optional step if your business objectives are specific. In such cases, you can fine-tune the pre-trained LLM on a smaller data set tailored to your domain. This process helps the model to adapt to your specific use case and improve performance.
9. UseOnce the performance is satisfactory, it is ready for integration into the desired application or service. This can include the setup of APIs that allow other programmes to interact with your language model. 
10. Maintenance and improvementIt is necessary to keep up with the latest advances in the field and consider retraining your model with new data or improved techniques to maintain and improve its effectiveness. 

Training large language models: a question of a company's resources and objectives

Large Language Models have proven to be a valuable asset for organisations in various fields. The decision to proceed with training should be based on the ability of existing models to adequately capture the use case, as well as 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.