QV May Be Enough: Toward the Essence of Attention in LLMs

TL;DR

This paper offers a theoretical and empirical analysis of the QKV mechanism in Transformers, proposing a simplified QV paradigm and optimization scheme, enhancing understanding and potential improvements of LLM architectures.

Contribution

It introduces the QV paradigm and QV-Ka optimization, providing a unified theoretical framework and empirical validation for the QKV mechanism in LLMs.

Findings

Empirical evidence supports the QV paradigm.

The QV-Ka scheme improves model efficiency.

Theoretical analysis clarifies the essence of attention mechanisms.

Abstract

Starting from first principles and a linguistic perspective centered on part-of-speech (POS) and syntactic analysis, this paper explores and derives the underlying essence of the Query-Key-Value (QKV) mechanism within the Transformer architecture. Based on this theoretical foundation, we provide a unified explanatory framework for the efficacy of contemporary architectures, including MQA, GQA, and MLA, while identifying their inherent trade-offs and potential optimization trajectories. We introduce the QV paradigm and provide empirical evidence for its validity. Building upon this, we propose the QV-Ka optimization scheme, which is further substantiated through experimental validation. The interpretable theoretical analysis of the QKV mechanism presented in this work establishes a robust foundation for the future evolution of large language model architectures.