Selective Attention: Enhancing Transformer through Principled Context Control

TL;DR

This paper introduces Selective Self-Attention (SSA), a lightweight enhancement to transformers that uses temperature scaling to control attention sparsity and relevance, improving language modeling performance.

Contribution

The paper proposes SSA, a novel method that applies principled temperature scaling to attention mechanisms, enabling better control over contextual relevance and sparsity in transformers.

Findings

SSA improves language modeling accuracy across benchmarks.

Temperature scaling reduces attention dilution and noise.

Lightweight with less than 0.5% additional parameters.

Abstract

The attention mechanism within the transformer architecture enables the model to weigh and combine tokens based on their relevance to the query. While self-attention has enjoyed major success, it notably treats all queries $q$ in the same way by applying the mapping $V^\top\text{softmax}(Kq)$, where $V,K$ are the value and key embeddings respectively. In this work, we argue that this uniform treatment hinders the ability to control contextual sparsity and relevance. As a solution, we introduce the $\textit{Selective Self-Attention}$ (SSA) layer that augments the softmax nonlinearity with a principled temperature scaling strategy. By controlling temperature, SSA adapts the contextual sparsity of the attention map to the query embedding and its position in the context window. Through theory and experiments, we demonstrate that this alleviates attention dilution, aids the optimization process, and enhances the model's ability to control softmax spikiness of individual queries. We also incorporate temperature scaling for value embeddings and show that it boosts the model's ability to suppress irrelevant/noisy tokens. Notably, SSA is a lightweight method which introduces less than 0.5% new parameters through a weight-sharing strategy and can be fine-tuned on existing LLMs. Extensive empirical evaluations demonstrate that SSA-equipped models achieve a noticeable and consistent accuracy improvement on language modeling benchmarks.