In recent years, large language models, known as LLMs, have become commonplace across many industries.

Generative AI models, such as ChatGPT, are powered by extensively trained LLMs. Whilst you would need rather large pockets to train a large language model to rival ChatGPT (Open AI estimated it took $20 million and 55 days to complete the training process for GPT-4), there are many smaller LLMs that could prove useful for your business.

If you are looking to build and train your own large language model, here’s how.

The 3 steps to Building and Training a Large Language Model

Step One: The pre-training phase

1) The success and accuracy of your LLM will depend on your initial dataset. You will need to collect a diverse, large dataset relevant to the purpose of the model you intend to build. You will want to ensure there is diversity amongst the sources of the data, to ensure your model will be able to learn and understand a wide range of language patterns, rather than becoming too specialised.

2) Next, you will need to clean the data, to remove any formatting issues, irrelevant information and “noise”. In the context of data and model training, noise is the unwanted information that could distort, skew or corrupt the data. Almost all data will contain noise, so this step is vital. If you do not remove the noisy data, you risk ending up with a false sense of accuracy, or the model making false conclusions.

3) Once you have cleaned the text data, you will need to tokenize the data. A token is a randomized string of numbers that a computer can understand, but has no exploitable value or meaning. Tokenization is a key practice for data security. For model training, tokenization helps to break text into smaller parts, which makes for easier machine analysis into patterns and relationships.

4) The pre-training of large language models involves training the model to predict a word within a sequence of text, using text from the cleaned dataset. This process helps to train a model that will understand and be able to generate human-like language.

Step two: Supervised instruction tuning

This stage of the large language model building phase involves the model being provided with the user’s message as the input, as well as the desired response as the target set by a human labeller.

The model will be trained to minimise the difference between its predictions and the targeted response provided by the labeler.

It is within this stage that the model begins to understand what the instruction means, as well as displaying recall and memory, to retrieve knowledge based on the instruction model.

Step three: Reinforcement learning from human feedback

Reinforcement learning from human feedback, or RLHF, is a technique used within machine learning to help train models to output results that align with human interests and preferences. RLHF is used extensively within generative AI models such as chatbots and text-to-speech models.

RLHF is a second stage of fine-tuning a model, and is designed primarily to accommodate the three H’s of model training, which state a model should be trained to be helpful, honest and harmless.

The RLHF stage of LLM building

The model will be instructed to output multiple responses to the same prompt. The human labeler responsible for training the model will then rank the responses from best to worst. Using this feedback, a further ‘reward’ model will be trained. Reward models are a form of machine learning model that assess how well a model can generate responses that match human preferences. They are trained on the basis of reinforcement learning, where an agent will learn to make decisions based on either a reward or a punishment. These reward models are an essential part of building LLMs as they help to align the output with the desired intentions.

Once this reward model is trained, it can replace the human labeler in label data and provide feedback to the model. This stage will enable LLMs to be trained at a significant scale.

What can LLMs be used for?

LLMs can be built to serve a wide variety of applications seeking to analyze, understand and generate human language within the digital space.

Examples include:

• Sentiment analysis
• Data analysis on a global scale
• Custom chatbots and customer support models
• Content creation
• Responsive education and learning tools
• Healthcare tools
• Legal document analysis
• Research assistant to summarize long documents

Case Study: Assistant Robert

NetGeist created a proprietary website chatbot for Neurotechnology known as Assistant Robert. This AI chatbot can answer inquiries about the company and the range of products, as well as providing general information about the company.

Assistant Robert provides 24/7 operations without interruptions. NLP team lead Vytas Mulevičius stated that “Our mission was to create a personalized chatbot that would answer user inquiries about Neurotechnology’s solutions and serve as a virtual guide, simplifying website navigation and offering accurate and reliable answers.

Assistant Robert’s ability to operate 24/7, together with its growing knowledge base, ensures that users can get the information they need anytime, anywhere.”

Try Assistant Robert out for yourself on the Neurotechnology website!