Training loss

Training loss is a measure of how well a model fits the training data, or in other words, how accurately it can predict the next token given the previous ones. The lower the training loss, the better the model fits the data.

Limitations

Training loss alone is not enough to evaluate the performance of a model, as it does not tell us how well the model can generalize to new and unseen data. For that, we need to use validation loss, which is the measure of how well the model fits a separate set of data that is not used for training.

Role in Training

The training process of an LLM involves feeding batches of text sequences to the model and computing the training loss for each batch. The training loss is calculated by comparing the model's predictions with the actual next tokens in the sequences. The difference between the predictions and the actual tokens is called the error. The training loss is then the sum of errors for all tokens in a batch. The model then updates its parameters using an optimization algorithm, such as gradient descent, to minimize the training loss. Training loss is monitored throughout the training process to see how well the model is learning from the data. Ideally, the training loss should to decrease over time, indicating that the model is improving its predictions. Factors that can affect the training loss include:

• The size and quality of the training data: The more and better data we have, the more likely the model is to learn from it. However, if the data is too large or noisy, it can also make the training process slower or harder¹.
• The size and architecture of the model: The larger and more complex the model is, the more parameters it has to learn from the data. However, if the model is too large or complex, it can also overfit the data, meaning that it memorizes specific patterns in the training data but fails to generalize to new data.
• The learning rate and other hyperparameters: The learning rate is a parameter that controls how much the model changes its parameters in each update. If the learning rate is too high, the model can overshoot the optimal solution and cause instability or divergence in the training loss. If the learning rate is too low, the model can take too long to converge or get stuck in a local minimum. Other hyperparameters, such as batch size, dropout rate, etc., can also affect the training loss and performance of the model.

Therefore, training LLMs requires careful tuning and experimentation with different settings and strategies to achieve optimal results. We also need to compare the training loss with the validation loss to ensure that our model is not overfitting or underfitting⁴. Additionally, we can use other metrics and benchmarks to evaluate our model's performance on specific tasks and domains¹²³.

See Also

• LLaMA
• Training and Validation Loss in Deep Learning - Baeldung. [1]
• arXiv:2302.13971v1 [cs.CL] 27 Feb 2023. https://arxiv.org/pdf/2302.13971v1.pdf.
• Replit - How to train your own Large Language Models. https://blog.replit.com/llm-training.
• ChatGPT and large language models: what's the risk?. https://www.ncsc.gov.uk/blog-post/chatgpt-and-large-language-models-whats-the-risk.