Causal Attention Mask

Causal attention masks are a crucial component in autoregressive transformer models, dictating the flow of information during sequence processing by preventing the model from "seeing" future tokens. Current research focuses on refining causal masking techniques within various architectures, including LLMs and Vision Transformers, to mitigate issues like position bias and improve performance on tasks such as video understanding, image generation, and long-context language modeling. These improvements enhance the capabilities of large language models and multimodal models, leading to more accurate and efficient processing of complex sequential data in diverse applications. The ongoing work aims to optimize causal masking for better generalization, reduced computational cost, and improved factual consistency in generated outputs.