Title: Alleviating Forgetfulness of Linear Attention by Hybrid Sparse Attention and Contextualized Learnable Token Eviction
Primary area: foundation or frontier models, including LLMs
Keywords: linear attention, language modeling, sparse attention, token eviction

Abstract: Linear-attention models that compress the entire input sequence into a fixed-size recurrent state offer an efficient alternative to Transformers, but their finite memory induces forgetfulness that harms retrieval-intensive tasks.
To mitigate the issue, we explore a series of hybrid models that restore direct access to past tokens. We interleave token mixers of intermediate time and space complexity between linear attention and full attention, including the query-aware native sparse attention, and sparse attention with token eviction. We further propose a novel learnable token eviction module. Combined with sliding-window attention, an end-to-end trainable lightweight CNN-based eviction module aggregates information from both past and future adjacent tokens to adaptively retain a limited set of critical KV-pairs per head, maintaining linear attention's constant time and space complexity. Empirical evaluations on retrieval-intensive benchmarks support the effectiveness of our approaches.

TL;DR: Enhanced retrieval performance of subquadratic time/constant space models by the hybrid of linear attention and query-aware sparse attention or our novel learnable token eviction network that evicts KV cache based on past and future local contexts.