arxiv_cl 95% Match Research Paper ML Researchers,AI Engineers,NLP Practitioners,Developers working with LLMs 17 hours ago

Path-Consistency with Prefix Enhancement for Efficient Inference in LLMs

large-language-models › reasoning

📄 Abstract

Abstract: To enhance the reasoning capabilities of large language models (LLMs), self-consistency has become a popular approach, combining multiple samplings with majority voting. However, current methods are computationally expensive and time-consuming due to the need for numerous samplings. To address this, this paper introduces path-consistency, which leverages the confidence of earlier-generated answers to identify the most promising prefix and guide the generation of subsequent branches. By dynamically guiding the generation of subsequent branches based on this prefix, path-consistency mitigates both the errors and redundancies from random or less useful sampling in self-consistency. This approach reduces errors and redundancies from random sampling, significantly accelerating inference by minimizing token consumption. Our extensive empirical results demonstrate that path-consistency improves inference latency by up to 40.5\%, while maintaining task accuracy across various tasks, including mathematical reasoning, commonsense reasoning, and symbolic reasoning.

Key Contributions

Path-consistency with prefix enhancement is introduced as a novel method to accelerate LLM inference and improve reasoning. By leveraging the confidence of early outputs to guide subsequent generation branches, it mitigates errors and redundancies from random sampling in self-consistency, significantly reducing inference latency (up to 40.5%) while maintaining task accuracy.

Business Value

Enables faster and more cost-effective deployment of LLMs for complex reasoning tasks, making them more practical for real-time applications and resource-constrained environments.

Paper Metadata

Innovation Type

Algorithmic innovation

Deployment Feasibility

High, as it directly addresses inference efficiency, a critical factor for deployment.

Limitations Addressed

The computational expense and time-consuming nature of existing self-consistency methods for enhancing LLM reasoning.

Performance Gains

Up to 40.5% improvement in inference latency while maintaining task accuracy.

Technical Tags

LLM inferenceself-consistencypath-consistencyprefix enhancementreasoning capabilitiesinference accelerationtoken reductioncomputational efficiency

Research Topics

Large Language ModelsEfficient AIMachine Learning OptimizationReasoning in AIInference Techniques

Methods & Architectures

Path-ConsistencyPrefix EnhancementConfidence-based branch guidanceDynamic branch generation Large Language Models (LLMs)

Applications & Tasks

Natural Language Processing AI Inference Optimization Reducing LLM inference cost and timeImproving efficiency of self-consistency methodsEnhancing reasoning accuracy with reduced sampling Accelerating LLM inferenceImproving reasoning performanceMinimizing token consumption during generation

Related Fields

Machine LearningDeep LearningNatural Language ProcessingArtificial Intelligence

Keywords

LLMinferenceefficiencyreasoningself-consistencypath-consistencyprefix enhancementlatencytoken reductioncomputational costsamplingmajority voting

Academic Context

#Large Language Models#Efficient AI#Machine Learning Optimization#Reasoning in AI#Inference Techniques

Commercial Potential

Potential Products

Optimized LLM inference enginesLibraries for faster LLM reasoning

Target Industries

TechnologySaaSCustomer ServiceContent Creation

Use Case Examples

Deploying LLMs for real-time question answering or complex problem-solving with reduced latency.Enabling LLM-powered applications on devices with limited computational power.

Competitive Edge

Path-consistency offers a novel and more efficient alternative to self-consistency for improving LLM reasoning, achieving significant speedups with comparable or better accuracy.

Market Opportunity

The market for efficient LLM inference is rapidly growing.

Revenue Models

Licensing of optimized modelsAPI access to efficient inference services.

Resource Requirements

Compute Needs

Moderate for training/fine-tuning, but significantly reduced for inference compared to self-consistency.

Data Requirements

Requires datasets suitable for reasoning tasks where self-consistency is typically applied.

Deployment Constraints

Requires careful implementation of the path-consistency logic.,Effectiveness might vary across different LLM architectures and tasks.

Scalability

The method is designed to improve scalability by reducing inference costs.

Production Readiness

Maturity Level

Research

Time to Market

1-2 years for optimized inference libraries

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