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TELL-TALE: Task Efficient LLMs with Task Aware Layer Elimination

large-language-models › model-architecture

📄 Abstract

Abstract: In this paper we introduce Tale, Task-Aware Layer Elimination, an inference-time algorithm that prunes entire transformer layers in an LLM by directly optimizing task-specific validation performance. We evaluate TALE on 9 tasks and 5 models, including LLaMA 3.1 8B, Qwen 2.5 7B, Qwen 2.5 0.5B, Mistral 7B, and Lucie 7B, under both zero-shot and few-shot settings. Unlike prior approaches, TALE requires no retraining and consistently improves accuracy while reducing computational cost across all benchmarks. Furthermore, applying TALE during finetuning leads to additional performance gains. Finally, TALE provides flexible user control over trade-offs between accuracy and efficiency. Mutual information analysis shows that certain layers act as bottlenecks, degrading task-relevant representations. Tale's selective layer removal remedies this problem, producing smaller, faster, and more accurate models that are also faster to fine-tune while offering new insights into transformer interpretability.

Authors (3)

Omar Naim

Krish Sharma

Nicholas Asher

Submitted

October 26, 2025

arXiv Category

cs.LG

arXiv PDF

Key Contributions

TALE (Task-Aware Layer Elimination) is an inference-time algorithm that prunes entire transformer layers in LLMs by optimizing task-specific validation performance. It requires no retraining, consistently improves accuracy while reducing computational cost, and even enhances fine-tuning efficiency, offering flexible trade-offs between accuracy and efficiency.

Business Value

Significantly reduces the operational costs and latency of deploying LLMs, making them more accessible and practical for a wider range of real-time applications.

Paper Metadata

Innovation Type

Algorithmic Improvement

Deployment Feasibility

High. TALE is an inference-time algorithm, meaning it can be applied to pre-trained models without requiring retraining, making deployment straightforward.

Limitations Addressed

High computational cost and latency of large language models during inference, and the need for retraining to optimize for specific tasks.

Performance Gains

Consistently improves accuracy while reducing computational cost across benchmarks. Additional performance gains when applied during fine-tuning.

Technical Tags

Task-Aware Layer Elimination (TALE)Large Language Models (LLMs)Inference-time PruningTransformer LayersTask-Specific ValidationZero-shot LearningFew-shot LearningComputational Cost ReductionAccuracy ImprovementFine-tuning Efficiency

Research Topics

LLM EfficiencyModel CompressionInference OptimizationTask-Specific AdaptationTransformer Architectures

Methods & Architectures

Task-Aware Layer Elimination (TALE)Direct optimization of task-specific validation performanceLayer pruning at inference time Transformer (LLMs)LLaMA 3.1Qwen 2.5Mistral 7BLucie 7B

Applications & Tasks

Natural Language Processing Text Generation Information Extraction Any LLM application High computational cost of LLMsNeed for faster inferenceImproving accuracy while reducing costBottlenecks in transformer layers Reduce computational cost of LLMsImprove LLM accuracyAccelerate LLM fine-tuningOptimize LLM inference

Datasets & Benchmarks

Benchmarks

9 tasks • 5 models (LLaMA 3.1 8B, Qwen 2.5 7B, Qwen 2.5 0.5B, Mistral 7B, Lucie 7B)

AccuracyComputational costInference speedFine-tuning time

Related Fields

Machine LearningDeep LearningNatural Language ProcessingModel Optimization

Keywords

LLMTransformerLayer PruningInference OptimizationTask-SpecificEfficiencyAccuracyComputational CostFine-tuningZero-shotFew-shotTALE

Academic Context

#LLM Efficiency#Model Compression#Inference Optimization#Task-Specific Adaptation#Transformer Architectures

Commercial Potential

Potential Products

Optimized LLM inference enginesLibraries for efficient LLM deployment

Target Industries

TechnologySoftware DevelopmentCloud ComputingSaaS providers

Use Case Examples

Deploying LLMs on resource-constrained devicesReducing server costs for large-scale LLM servicesEnabling faster response times for AI-powered applications

Competitive Edge

Offers a unique inference-time pruning method that directly optimizes for task performance, potentially outperforming other compression techniques that require retraining.

Market Opportunity

Huge and growing market for efficient LLM deployment solutions.

Revenue Models

Licensing of the TALE algorithmintegration into commercial LLM platformsor offering optimized LLM services.

Resource Requirements

Compute Needs

Reduced compute requirements during inference compared to full models. Training/fine-tuning compute depends on the base model.

Data Requirements

Task-specific validation datasets for optimization.

Deployment Constraints

The effectiveness of pruning might vary across different tasks and models. Requires careful validation.

Scalability

Improves scalability by reducing the computational footprint of LLMs.

Production Readiness

Maturity Level

Research

Time to Market

6-18 months for integration into LLM deployment frameworks.

Patent Potential

Moderate, for the specific TALE algorithm and its optimization process.

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