In the context of machine learning and natural language processing, a "Transformer" refers to a specific type of neural network architecture introduced in the paper "Attention Is All You Need" by Vaswani et al. in 2017. Transformers have become the foundation for many state-of-the-art models, including BERT, GPT, T5, and more.
Losses play a crucial role in training neural networks, including Transformers. They are used to quantify the difference between the predicted output of the model and the actual target output. The goal of training is to minimize these losses, which involves adjusting the model's parameters through optimization techniques.
In a Transformer architecture, there are several types of losses that are commonly used:
Cross-Entropy Loss (or Softmax Loss): This is one of the most common loss functions used in classification tasks. It measures the difference between the predicted probability distribution over classes and the actual class labels. In the context of language modeling, it helps the model generate more accurate next-token predictions. For example, in text generation, the model's output at each position is compared to the actual token at that position.
Masked Language Model (MLM) Loss: In models like BERT, the MLM loss is used to pretrain the model by predicting masked words in a sentence. The model learns to predict the masked words based on the context provided by the surrounding words. The loss encourages the model to capture meaningful relationships between words.
Seq2Seq Loss (Sequence-to-Sequence Loss): In tasks like machine translation or text summarization, where the goal is to convert one sequence of tokens into another, the Seq2Seq loss measures the discrepancy between the predicted sequence and the target sequence.
Reinforcement Learning Loss: Some advanced applications of Transformers, like text generation in reinforcement learning setups, might use a reinforcement learning loss. This involves using rewards or penalties to guide the model's generation process, encouraging it to generate desired outputs.
Perplexity: While not a traditional loss function, perplexity is often used as an evaluation metric for language models. It quantifies how well the model predicts a sequence of tokens. Lower perplexity indicates better performance.
Custom Loss Functions: Depending on the specific task and model modifications, researchers might develop custom loss functions that align with the model's objectives. These could involve additional objectives or regularization terms.
Remember that the choice of loss function depends on the specific task you're training your Transformer model for. Different tasks require different loss functions to guide the learning process effectively.