ForwardMasked

ForwardMasked is a technique used in natural language processing, particularly within the context of masked language modeling (MLM). MLM, popularized by models like BERT, involves training a neural network to predict randomly masked tokens in a sequence based on the surrounding unmasked tokens. ForwardMasked specifically refers to a scenario where the model can only utilize the tokens that appear *before* the masked token in the sequence to make its prediction. This stands in contrast to bidirectional masking, where the model can use tokens both before and after the masked token.

The application of ForwardMasked is often seen in tasks where the generation of text needs to be sequential or causal, such as in autoregressive language models. By restricting the model's view to only preceding context, it learns to predict the next token based on what has already been generated. This approach is crucial for tasks like text generation, summarization, and machine translation, where the output is built token by token. While bidirectional masking allows for a richer understanding of context for tasks like sentiment analysis or question answering, ForwardMasked is more aligned with the principles of generating coherent and contextually appropriate sequences. The effectiveness of ForwardMasked depends heavily on the specific architecture and training objective of the language model employed.