In the context of training a causal language model (CLM) using sequences with EOS tokens, it’s important to note that the presence of the EOS token primarily serves as a signal during training. It helps the model learn the concept of sentence boundaries but does not explicitly instruct the model to stop generating text.
When multiple sentences are concatenated with multiple EOS tokens in a single training sequence, the model indeed continues generating text after each EOS token. However, the training process still allows the model to learn the patterns and dependencies within and across sentences. Over time, the model can learn to generate coherent and meaningful text, even if there are multiple EOS tokens within the training sequence. The process of learning to stop generating text at appropriate points is a result of the model’s training on a large dataset that contains diverse examples of sentence structures.
Regarding the special tokens in OpenAI’s GPT-2 model, it is important to understand that different language models may have different configurations for their special tokens. In the case of GPT-2, the absence of a padding token and the same tokens for BOS (beginning of sequence), EOS (end of sequence), and UNK (unknown) indicate the model’s specific design choices.
While padding tokens can be useful in certain applications, GPT-2 does not use padding tokens as it does not require fixed-length input sequences. Instead, the model processes input dynamically based on the actual length of the input sequence.
Regarding the BOS, EOS, and UNK tokens being the same, it suggests that GPT-2 does not differentiate between these special tokens and treats them as equivalent. This means that the model does not assign any special meaning to these tokens during generation.
It’s important to note that the absence of a padding token and the design choice of treating BOS, EOS, and UNK tokens as the same do not impact the model’s ability to generate text effectively or learn meaningful representations from the training data. The model’s training process and the patterns within the data enable it to generate coherent and contextually relevant text.