Title: Short Window Attention Enables Long-Term Memorization
Primary area: foundation or frontier models, including LLMs
Keywords: hybrids, xLSTM, SWA, memory, attention, long-context, architecture-design, LLM, stochastic

Abstract: Recent works show that hybrid architectures combining sliding window softmax attention layers with linear recurrent neural network (RNN) layers outperform both of these architectures taken separately. However, the impact of the window length and the interplay between softmax attention and linear RNN layers remain under-studied. In this work, we introduce SWAX, a hybrid architecture consisting of sliding-window attention and xLSTM linear RNN layers. 

A counter-intuitive finding with SWAX is that larger sliding windows do not improve the long-context performance. In fact, short window attention encourages the model to better train the long-term memory of the xLSTM, by relying less on the softmax attention mechanism for long context-retrieval. 

The issue with small sliding windows is that they are detrimental for short-context tasks, which could be solved with information from moderately larger sliding windows otherwise. Therefore, we train SWAX by stochastically changing the sliding window size, forcing the model to leverage both a longer context window and the xLSTM memory. SWAX trained with stochastic window sizes significantly outperforms regular window attention both on short and long-context problems.

TL;DR: We evidence the counter-intuitive fact that, in hybrid architectures, shorter sliding windows lead to better length-extrapolation. We introduce a training procedure that allows the hybrids to perform well on both short and long-context tasks.