arxiv_ai 92% Match Research Paper ML Engineers,Researchers in LLMs,Developers deploying models on edge devices 4 weeks ago

How Many Parameters Does Your Task Really Need? Task Specific Pruning with LLM-Sieve

large-language-models › training-methods

📄 Abstract

Abstract: As Large Language Models (LLMs) are increasingly deployed for narrow tasks in resource-constrained settings, a central question arises: how much of an LLM is truly necessary for a given task? We present LLM-Sieve, a framework that prunes LLMs down to the minimal parameter subset needed to preserve task performance. Our approach introduces two innovations: (i) output-aligned non-orthogonal projections, which yield more faithful low-rank approximations than traditional PCA/SVD by aligning directly with layer outputs; and (ii) adaptive pruning via a Genetic Algorithm, which automatically discovers matrix-specific pruning levels and exposes the uneven distribution of task-relevant knowledge. Across models from 3.8B to 70B parameters, LLM-Sieve removes 20-75% of weights with only 1-5% accuracy loss-substantially ahead of prior pruning methods. Beyond efficiency, our framework reveals bottleneck matrices that concentrate critical knowledge, suggesting architectural implications for future LLM design. LLM-Sieve integrates seamlessly with LoRA fine-tuning and quantization, enabling both efficient deployment and deeper understanding of knowledge organization in LLMs.

Key Contributions

Introduces LLM-Sieve, a framework for task-specific LLM pruning that removes up to 75% of weights with minimal accuracy loss (1-5%). It uses output-aligned projections and a Genetic Algorithm for adaptive pruning, revealing bottleneck matrices that concentrate critical knowledge.

Business Value

Enables efficient deployment of powerful LLMs on edge devices and reduces inference costs, broadening the applicability of LLMs in various industries and applications.

Paper Metadata

Innovation Type

Pruning Framework and Optimization Technique

Deployment Feasibility

High, as it directly addresses deployment challenges.

Limitations Addressed

The significant size and computational requirements of LLMs, making them difficult to deploy in resource-constrained settings.

Performance Gains

Substantial weight reduction with minimal impact on task performance, significantly outperforming existing pruning methods.

Technical Tags

LLM pruningtask-specific pruningparameter efficiencylow-rank approximationgenetic algorithmbottleneck matricesweight reductionaccuracy preservation

Research Topics

Model PruningLLM EfficiencyParameter OptimizationTask-Specific Adaptation

Methods & Architectures

LLM-Sieve frameworkoutput-aligned non-orthogonal projectionsadaptive pruning via Genetic AlgorithmPCA/SVD comparison Large Language Models (LLMs)

Applications & Tasks

Resource-Constrained Environments Edge AI Efficient LLM Deployment LLM Over-parameterizationFinding Minimal Parameter SubsetsTask-Specific Knowledge Concentration Pruning LLMs to minimal sizePreserving task performance after pruning

Datasets & Benchmarks

Benchmarks

20-75% weight removal • 1-5% accuracy loss • Outperforms prior pruning methods

Accuracy lossWeight reduction percentage

Related Fields

Machine LearningDeep LearningModel CompressionNatural Language Processing

Keywords

LLM pruningtask-specificparameter efficiencylow-rank approximationgenetic algorithmbottleneck matricesweight reductionaccuracy preservationresource-constrainededge AImodel compressionLLM-Sieve

Academic Context

#Model Pruning#LLM Efficiency#Parameter Optimization#Task-Specific Adaptation

Commercial Potential

Potential Products

LLM optimization toolsLibraries for efficient LLM deployment on edge devices

Target Industries

Mobile ComputingIoTAutomotiveAI Infrastructure

Use Case Examples

Deploying LLMs on smartphones for on-device AI featuresReducing cloud inference costs for large-scale applicationsEnabling real-time LLM capabilities in embedded systems

Competitive Edge

Offers a novel, adaptive pruning framework that achieves superior weight reduction while preserving task performance, identifying critical knowledge bottlenecks.

Market Opportunity

Large and growing market for efficient AI and edge computing solutions.

Revenue Models

Licensing of the LLM-Sieve technologyintegration into model optimization services.

Resource Requirements

Compute Needs

Moderate (for the pruning process), significantly reduced inference compute.

Data Requirements

Requires task-specific datasets for evaluating pruning effectiveness.

Deployment Constraints

The pruning process itself can be computationally intensive.

Scalability

Aims to improve scalability by reducing model size and computational requirements.

Production Readiness

Maturity Level

Research/Development

Time to Market

1-2 years (for integration into optimization tools)

Patent Potential

Moderate (novel pruning framework and projection method)

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