How Do You Build a Large Language Model?

TruEra How Do You Train a Large Language Model Featured image 1200x630

With the advent of large language models (LLMs), machines can interact with human language in ways that weren’t possible before. This breakthrough will reshape every industry, from financial services, to healthcare and entertainment. No wonder why ChatGPT quickly got so popular – it revealed to the general public the incredible potential of LLMs. At times, its ability to converse fluently might even look magical, even though there is no trick here, only great engineering. 

Yet, one may wonder, what’s hiding under the hood? More broadly, how do you build a large language model ? What challenges may you face in the training and alignment phases and how to overcome them? We are addressing these basic but fundamental questions in this blog post.

Phase 1: Pre-training

LLMs are machine learning models that aim to predict and generate plausible language, after being trained on a massive amount of text data. These models have the ability to generate coherent responses to questions, and even mimic different writing styles. Building a large language model is a complex process that involves two main phases: pre-training and alignment. This initial phase is crucial as it imparts general language understanding to the model and is generally structured around six steps:

  • Step 1 – Data collection and preprocessing: gather a vast and varied dataset from the internet, including billions of text sources. This can include books, articles, websites, and more. Preprocess the data by tokenizing the text into smaller units (words or subwords), removing irrelevant content, and organizing it for efficient training.
  • Step 2 – Designing the transformer architecture: select a suitable variant of the transformer architecture, like the generative pre-trained transformer (GPT) architecture. This architecture consists of multiple layers of self-attention mechanisms and feed-forward neural networks that enable the model to understand context and relationships between words.
  • Step 3 – Model initialization Initialize the model with random weights, creating a blank slate for learning. These weights will be adjusted during the training process through backpropagation.
  • Step 4 – Pre-training objective: define a pre-training objective, typically a language modeling task. The model learns to predict the next word in a sentence given the preceding words, fostering an understanding of syntax, grammar, and context.
  • Step 5 – Training: feed the pre-processed data into the model and train it using gradient-based optimization techniques like stochastic gradient descent. The model’s parameters are updated iteratively to minimize the difference between predicted and actual next words.
  • Step 6: Model Size and Scaling: Model size is a critical consideration. Larger models can capture more intricate patterns but require more computational resources. Balancing computational capacity with model size is essential. Bigger models have more parameters, allowing them to learn a wider range of patterns, but they also demand more training time and data.

Phase 2: Alignment

In the alignment phase, the model is adapted to specific tasks through fine-tuning on task-specific data:

  • Step 1 – Task definition: determine the target task you want the model to perform, such as text generation, translation, sentiment analysis, etc.
  • Step 2 – Task-specific data collection: Gather a dataset specifically tailored to your target task. 
  • Step 3 – Fine-tuning objective: design a task-specific objective, which guides the model’s learning during fine-tuning. For example, in sentiment analysis, the objective might be to correctly predict sentiment labels.
  • Step 4 – Fine-tuning: continuous training of the model using the task-specific dataset while keeping the weights learned during pre-training frozen. This allows the model to retain its general language understanding while adapting to the task-specific data.
  • Step 5 – Evaluation and iteration: evaluate the fine-tuned model’s performance on validation data. Fine-tuning might require several iterations of parameter adjustments to achieve optimal task performance.
  • Step 6 – Model deployment: once you’re satisfied with the fine-tuned model’s performance, you can deploy it for your intended task, benefiting from both the model’s general language understanding and its task-specific adaptability.

However, some models are released without this alignment phase, providing a more generalized language understanding but limited task-specific performance. 


Building large language models comes with its own set of challenges, which require careful consideration:

  • Time and cost: Training a large language model is a rather lengthy and costly process. Yet, to address this challenge, various solutions have emerged, including leveraging cloud computing services platforms Amazon Web Services, Google Cloud Platform, and Azure for on-demand resources. Plus, libraries such as PyTorch and TensorFlow enable distributed training which significantly accelerate the entire process. Another relevant option is to use pre-trained models for specific tasks to reduce the time and data required for fine-tuning. 
  • Risk of Bias: Large language models learn from the data they are trained on, and thus could inherit and amplify biases. This can perpetuate societal biases and may lead to discriminatory or unfair outcomes. That is the reason why AI bias is a top concern for industry practitioners. To address this challenge, they use various bias mitigation techniques including data augmentation, fairness constraints, alignment and model refusal. The last two techniques are particularly important. Alignment with human values can be achieved through continuous feedback and reinforcement learning, enabling the model to improve its responses in real-world contexts and reduce biased outputs. On the other hand, model refusal acts as a safeguard by allowing the model to decline generating content when it detects potential biases, harmful, or fake information. Finally, AI developers are encouraged to run regular audits and on-going monitoring of their models.
  • Risk of producing harmful or fake content: As language models become more sophisticated, there is a concern that they could be exploited to generate harmful or fake content, including misinformation, hate speech, or deepfakes. Ensuring responsible use of language models involves developing mechanisms to detect and prevent the generation of harmful or malicious outputs. One popular approach to overcome this challenge is to incorporate data filtering and preprocessing measures to eliminate offensive or inappropriate content from the training data. Additionally, employing adversarial training techniques can enhance the model’s ability to identify and reject toxic or malicious inputs.
  • Lack of transparency. Another key challenge is the opacity of LLMs. Determining the main drivers of their behaviors is just very difficult. As a consequence, identifying and addressing the root causes of model issues is a complex process. However, some researchers at Anthropic have recently demonstrated that it was possible to use influence functions (IF) to determine how a large language model’s parameters and outputs would change if a given sequence were added to the training set and thus partially explain its behavior. 


The advent of LLMs represents a significant progress in the AI industry and is poised to unleash a new wave of innovations. Yet, building a large language model is a multi-step process that involves generative pre-training, supervised learning and reinforcement learning with human feedback, and fine-tuning. It also comes with a set of complex challenges; that is the reason why only a limited number of organizations with deep resources have managed to build and put LLMs in production at the moment. Addressing these challenges will lead to an even faster democratisation of AI but it requires an industry wide effort. 

Last modified on November 8th, 2023