The promise of Large Language Models and Generative AI are alluring with the potential to significantly enhance self-service and internal knowledge sharing activities.

While the potential benefits are appealing, the time and costs are likely far too prohibitive for many – at least for now.

A Complex Process

The intent of this article is to illustrate the level of effort required to develop and train Large Language Models and is not intended to prescribe how to build and train them.

To highlight the level of complexity, I asked ChatGPT (GPT4 model) to:

provide an overview of the steps necessary to build and train a large language model.

Here is ChatGPT’s response:

1. 1. Define the objective: Clearly state the purpose and scope of the language model, which will guide the subsequent steps. Consider factors such as desired performance, domain specificity, and resource constraints.
   2. Collect and preprocess data: Gather a diverse and representative dataset of text for training. This dataset should be large enough to capture the complexity of the language and cover various domains. Preprocess the data to remove irrelevant information, correct inconsistencies, and tokenize the text into suitable units (e.g., words, subwords, or characters).
   3. Split the data: Divide the dataset into training, validation, and testing sets. The training set is used to update the model’s parameters, the validation set is used to fine-tune hyperparameters and monitor performance during training, and the testing set is used to evaluate the final model.
   4. Choose a model architecture: Select an appropriate neural network architecture for the language model, such as Transformer, LSTM, or GRU. The choice will depend on factors like the size of the dataset, computational resources, and desired performance.
   5. Initialize the model: Set initial values for the model’s parameters, either randomly or by using pre-trained weights from an existing model (transfer learning).
   6. Configure the training: Set hyperparameters like learning rate, batch size, number of layers, and attention heads. Establish a suitable loss function (e.g., cross-entropy) and an optimization algorithm (e.g., Adam, RMSprop).
   7. Train the model: Update the model’s parameters iteratively using the training data. Feed the input tokens into the model, compute the loss by comparing the model’s predictions to the target tokens, and adjust the parameters accordingly. Monitor the validation loss to detect overfitting and adjust hyperparameters as needed.
   8. Regularization and fine-tuning: Apply regularization techniques like dropout, weight decay, or layer normalization to prevent overfitting. Optionally, fine-tune the model on domain-specific data to improve its performance in a particular context.
   9. Evaluate the model: Assess the model’s performance on the testing set and other relevant benchmarks. Analyze the results to identify any limitations or biases in the model, and iterate the training process as needed.
   10. Deploy the model: Integrate the trained model into a suitable application or platform, ensuring it is optimized for the target environment (e.g., through model pruning or quantization). Monitor the model’s performance in production, addressing any issues or updating the model as needed.
   11. Maintain and update: Continuously monitor and update the model as new data becomes available or as performance degrades due to changes in language patterns. Retraining the model periodically helps maintain its relevance and accuracy.

Start Planning Now

The complexity involved with building and maintaining large language models may be beyond the reach of many companies today, but it does not mean that we can’t start thinking about how we can apply this technology in the future.

The journey to the ideal future state for self-service, knowledge sharing, and digital engagement requires a clear vision for the future, an understanding of your current state, and a roadmap to guide your journey.

Begin to think about your use cases.  To get started read: ChatGPT is Cool – Now, Let’s Make a Plan to Put It to Work. – ServiceXRG