arxiv_ai 95% Match Research Paper AI Researchers,ML Engineers,LLM Developers,Hardware Optimization Specialists 1 week ago

Key and Value Weights Are Probably All You Need: On the Necessity of the Query, Key, Value weight Triplet in Decoder-Only Transformers

large-language-models › model-architecture

📄 Abstract

Abstract: The Query, Key, Value weight triplet is a building block of current attention mechanisms in state-of-the-art LLMs. We theoretically investigate whether this triplet can be reduced, proving under simplifying assumptions that the Query weights are redundant, thereby reducing the number of non-embedding/lm-head parameters by over 8%. We validate the theory on full-complexity GPT-3 small architectures (with layer normalization, skip connections, and weight decay) trained from scratch, demonstrating that the reduced model achieves comparable validation loss to standard baselines. These findings motivate the investigation of the Query weight redundancy at scale.

Authors (2)

Marko Karbevski

Antonij Mijoski

Submitted

October 27, 2025

arXiv Category

cs.LG

arXiv PDF

Key Contributions

This paper theoretically proves that Query weights are redundant in the Query, Key, Value (QKV) triplet of attention mechanisms in decoder-only transformers, under simplifying assumptions. Empirical validation on GPT-3 small models confirms that reducing these weights leads to comparable validation loss, suggesting a potential to reduce non-embedding/LM-head parameters by over 8%.

Business Value

Reducing model parameters leads to smaller model sizes, faster training, lower inference costs, and potentially enables deployment on resource-constrained devices, making LLMs more accessible and economical.

Paper Metadata

Innovation Type

Theoretical Insight / Architectural Optimization

Deployment Feasibility

High. The proposed change is a modification to the architecture that can be implemented during training or fine-tuning.

Limitations Addressed

High parameter count and computational cost of large transformer models,Potential redundancy within the attention mechanism's weight structure

Performance Gains

Over 8% reduction in non-embedding/LM-head parameters, with comparable validation loss.

Technical Tags

transformer architectureattention mechanismquery key valueweight redundancydecoder-only transformersparameter reductionGPT-3validation losscomputational efficiency

Research Topics

LLM ArchitectureTransformer EfficiencyModel CompressionAttention MechanismsDeep Learning Theory

Methods & Architectures

Theoretical investigation of QKV tripletProof of Query weight redundancy (under simplifying assumptions)Empirical validation on GPT-3 small architecturesTraining from scratchReduced parameter models Decoder-Only TransformersGPT-3 (small architectures)

Applications & Tasks

Natural Language Processing AI Model Optimization Reducing the number of parameters in LLMsInvestigating the necessity of the Query, Key, Value weight tripletImproving computational efficiency of transformers Efficient LLM training and inferenceModel compression

Datasets & Benchmarks

Benchmarks

Comparable validation loss to standard baselines (GPT-3 small)

Validation lossParameter count reduction

Related Fields

Deep Learning ArchitecturesTransformer NetworksMachine Learning TheoryModel Compression

Keywords

transformerattention mechanismquery key valueQKVweight redundancydecoder-onlyLLMlarge language modelsparameter reductionmodel compressionGPT-3validation losscomputational efficiencyarchitecture

Academic Context

#LLM Architecture#Transformer Efficiency#Model Compression#Attention Mechanisms#Deep Learning Theory

Commercial Potential

Potential Products

More efficient transformer architecturesOptimized LLM librariesTools for model compression

Target Industries

TechnologyCloud ComputingSaaSMobile Computing

Use Case Examples

Deploying LLMs on edge devicesReducing server costs for large-scale LLM services

Competitive Edge

Challenges the standard QKV formulation, offering a more parsimonious architecture that maintains performance, potentially leading to more efficient LLMs.

Market Opportunity

Very Large (LLM market)

Revenue Models

Enabling more cost-effective LLM serviceslicensing optimized architectures.

Resource Requirements

Compute Needs

Moderate (for validation experiments)

Data Requirements

Standard datasets for LLM training (e.g., used for GPT-3).

Deployment Constraints

Requires modifying the transformer architecture implementation. Need to ensure the theoretical assumptions hold broadly.

Scalability

The findings suggest scalability improvements by reducing parameters, leading to more efficient scaling.

Production Readiness

Maturity Level

Theoretical/Experimental

Time to Market

Medium (requires integration into frameworks and validation at scale)

Patent Potential

Moderate (for architectural modifications)

View Full Paper Back to Papers