Is there a data leakage in causal masking?

Following this tutorial on training a causal language model from scratch. I found the source code for the model they use (GPT2). On line 195 we define “causal_mask”. I tried commenting out this line and defining a new “causal_mask” with the same shape but either all True or all False entries (instead of the triangle masking). Though, the model still learned in both cases to generate natural language. This is unexpected as if all the inputs are masked all the time the model should not learn to generate coherent text. Am I missing something or is there data leakage?

It’s possible that there is data leakage in your experiment when you commented out the causal masking in the GPT2 model. Causal masking is used to prevent the model from attending to future tokens during training, so that it can only rely on information from past tokens to predict the next token. Without causal masking, the model may be able to access information from future tokens, which could potentially result in better performance during training. However, this would also mean that the model is being trained on information it wouldn’t have access to during inference, which could lead to overfitting.

In your experiment, it’s possible that the model is still able to generate coherent text despite the absence of causal masking because it has learned to rely on other signals in the training data, such as the frequency and co-occurrence of certain words and phrases. However, it’s also possible that there is data leakage and the model is inadvertently learning to use future information during training.

To confirm whether there is data leakage, you could try training the model on a different dataset that doesn’t have a temporal structure, such as a bag-of-words dataset. If the model still performs well without causal masking in this case, it would suggest that there is indeed data leakage. Alternatively, you could try using a different model architecture that doesn’t rely on causal masking, such as a bidirectional model, and compare its performance to the GPT2 model with and without causal masking.

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