Linear Transformer

Linear transformers aim to overcome the quadratic complexity of standard transformers by employing linear attention mechanisms, thereby enabling efficient processing of long sequences. Current research focuses on improving the accuracy and in-context learning capabilities of these models through novel attention mechanisms (e.g., cosine similarity, differential attention), optimized training algorithms (e.g., delta rule, preconditioned gradient descent), and efficient positional encoding schemes. This work is significant because it addresses the scalability limitations of traditional transformers, opening avenues for more efficient and powerful large language models and their application to diverse tasks, including long-context modeling and scientific computing.