What technique improves the capture of temporal dependencies in LLMs?

Dynamic masking is a technique that enhances the ability of large language models (LLMs) to capture temporal dependencies within sequential data. By dynamically adjusting which tokens in a sequence are masked during training, this method allows the model to better learn the relationships and context between words over time. This is particularly important for tasks that involve understanding the flow of information in text, as it helps the model consider various contexts and which parts of the input might be more significant for predicting or generating the next token.

In contrast, techniques such as batch normalization and layer normalization primarily focus on stabilizing the training process and ensuring that the activations within the network maintain a consistent scale. While these normalization techniques can improve the overall training efficiency and performance of the model, they do not specifically target the capture of temporal dependencies in sequences.

Dropout regularization is employed to prevent overfitting by randomly disabling a fraction of the neurons during training, which helps enhance generalization. However, similarly to normalization techniques, dropout does not directly influence how temporal or sequential relationships are understood or processed in the model.

By implementing dynamic masking, LLMs can adaptively focus on different parts of the input data at various training stages, thereby improving their capacity to recognize and leverage the dependencies that exist in sequences