The invalid connections form when the model is trained on sequence-to-sequence tasks, where the target sequence is generated step-by-step, and the model should not have access to future tokens during training. This scenario is common in tasks such as text generation, machine translation, or any task where the model needs to generate an output sequence based on an input sequence.

Masking the invalid connections (future tokens) is necessary to prevent the model from "cheating" by attending to the correct tokens in the future, which would not be available during inference. By masking these connections, we ensure that the model learns to rely on the available context, i.e., the past and current tokens, to make predictions.

The masking technique used in the code snippet provided is known as "causal masking" or "autoregressive masking." It is common in transformer-based models, which have been very successful in various natural language processing tasks. This type of masking is critical in ensuring that the model is trained in a way that is consistent with how it will be used during inference, maintaining the autoregressive nature of the task.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence