Sequence Length

Sequence length, the number of tokens or elements processed by a model, is a critical factor influencing the performance and efficiency of various machine learning models, particularly large language models (LLMs). Current research focuses on improving the handling of long sequences through architectural modifications (e.g., altered attention mechanisms, dilated attention), optimized training strategies (e.g., progressive length increase, dynamic data sampling), and efficient parallelization techniques. Addressing limitations imposed by sequence length is crucial for enhancing the capabilities of LLMs in tasks requiring extensive contextual information, such as long-document summarization, and for improving the efficiency of training and inference processes.