Length Extrapolation

Length extrapolation in transformer-based models focuses on enabling these models to accurately process sequences significantly longer than those seen during training. Current research emphasizes improving positional encoding methods, exploring various kernel functions and adaptive or data-driven approaches within transformer architectures like Mamba and LLMs, to enhance the models' ability to generalize to longer inputs. This is crucial for handling real-world applications involving lengthy text data, such as legal documents or scientific papers, where existing length limitations hinder performance. Success in this area would significantly broaden the applicability of large language models.