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Quantitative LLM Judges

large-language-models › evaluation

📄 Abstract

Abstract: LLM-as-a-judge is a framework where a large language model (LLM) evaluates the output of another LLM. While LLMs excel at producing qualitative textual evaluations, they often struggle to predict human preferences and numeric scores. We propose quantitative LLM judges, which align evaluation scores of existing LLM judges to humans in a given domain using regression models. The models are trained to improve the score of the original judge using its rationale and score. We present four quantitative judges for different types of absolute and relative feedback, which showcases the generality and versatility of our framework. Our framework is more computationally efficient than supervised fine-tuning and can be more statistically efficient when human feedback is limited, which is expected in practice. We validate these claims empirically on four datasets using two base judges. Our experiments show that quantitative judges can improve the predictive power of existing judges through post-hoc modeling.

Authors (12)

Aishwarya Sahoo

Jeevana Kruthi Karnuthala

Tushar Parmanand Budhwani

Pranchal Agarwal

Sankaran Vaidyanathan

Alexa Siu

+6 more

Submitted

June 3, 2025

arXiv Category

cs.CL

arXiv PDF

Key Contributions

This paper proposes 'quantitative LLM judges' that align the evaluation scores of existing LLM judges to human preferences using regression models. By training on the LLM's rationale and score, these quantitative judges improve predictive power and efficiency compared to supervised fine-tuning, especially when human feedback is limited. The framework is versatile, demonstrated by four judges for different feedback types.

Business Value

Enables more reliable and efficient evaluation of AI-generated content and responses, leading to better quality control and faster iteration cycles in developing LLM-based products. This can reduce costs associated with human evaluation.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

High, as it builds upon existing LLM infrastructure and requires training regression models, which is computationally feasible.

Limitations Addressed

LLMs' difficulty in predicting human preferences and numeric scores,Computational inefficiency of supervised fine-tuning for judge alignment,Need for large amounts of human feedback

Performance Gains

Improved predictive power of existing judges

Technical Tags

LLM-as-a-judgeQuantitative EvaluationRegression ModelsHuman PreferencesScore AlignmentComputational EfficiencyLimited FeedbackGenerative ModelsTextual EvaluationDomain Adaptation

Research Topics

Large Language ModelsAI EvaluationHuman-AI InteractionMachine LearningNatural Language Processing

Methods & Architectures

Regression models for score alignmentLLM-as-a-judge frameworkTraining on rationale and score Large Language Models (LLMs)

Applications & Tasks

AI model development Content generation evaluation Chatbot development LLMs struggling to predict human preferences and numeric scoresAligning qualitative LLM evaluations with quantitative human judgments Evaluating LLM outputsPredicting human preferencesGenerating quantitative scores

Related Fields

Artificial IntelligenceNatural Language ProcessingMachine LearningHuman-Computer Interaction

Keywords

Large Language ModelsLLM EvaluationAI JudgesQuantitative AssessmentHuman PreferencesRegressionNatural Language ProcessingGenerative AIModel AlignmentComputational EfficiencyLimited DataFeedback Mechanisms

Academic Context

#Large Language Models#AI Evaluation#Human-AI Interaction#Machine Learning#Natural Language Processing

Commercial Potential

Potential Products

Automated LLM evaluation platformsQuality assurance tools for AI content generationFeedback systems for LLM fine-tuning

Target Industries

TechnologyMediaCustomer ServiceEducation

Use Case Examples

Automated grading of AI-generated essaysEvaluating chatbot responses for helpfulnessBenchmarking different LLM models

Competitive Edge

Offers a more efficient and potentially more accurate alternative to direct human evaluation or full supervised fine-tuning for aligning LLM judges with human preferences.

Market Opportunity

Rapidly growing market for LLM development and deployment tools.

Revenue Models

SaaS for evaluation platformslicensing of the quantitative judge models.

Resource Requirements

Compute Needs

Moderate for training regression models, low for inference.

Data Requirements

LLM outputs, their rationales, scores, and corresponding human preferences/scores.

Deployment Constraints

Requires access to a base LLM judge and the ability to train/deploy regression models.

Scalability

Scalable, as regression models are generally efficient.

Production Readiness

Maturity Level

Research

Time to Market

Short to Medium (can be integrated into existing LLM pipelines)

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