Evaluating LLM Personalization via Semantic Constraint Verification

2026-06-15

AI Summary

Existing approaches to evaluating personalization in large language models (LLMs) often rely on surface-level matching or costly LLM-as-a-judge methods, suffering from limited interpretability and efficiency. This work proposes the Natural Language Inference Constraint Verification (NLICV) framework, which maps sentence semantics into sets of truth conditions and leverages natural language inference (NLI) models to verify personalized constraints. The framework categorizes model behaviors into four distinct patterns: personalized, generalized, sycophantic, and failed responses. NLICV enables the first semantic-invariant, efficient, and interpretable evaluation method, precisely identifying critical reasoning grounds and providing faithful, human-understandable evidence. Experimental results demonstrate strong alignment with human annotations and achieve up to a 2100× speedup over LLM-as-a-judge, substantially reducing both latency and token consumption.

Abstract

Current evaluation paradigms for Large Language Model (LLM) personalization rely heavily on brittle surface-matching metrics or computationally expensive LLM-as-a-judge protocols, both of which lack interpretability. To address these limitations, we introduce Natural Language Inference Constraint Verification (NLICV), a scalable, semantically invariant framework that maps sentence meanings to truth-condition sets to verify personalization constraints via a Natural Language Inference (NLI) model. Moving beyond binary scoring, NLICV categorizes LLM behaviors into four distinct modes: personalization, generalization, sycophancy, and failure. Extensive experiments demonstrate that NLICV aligns closely with human annotations while drastically reducing the latency and token costs associated with LLM judges (up to 2100 inference speedup). Finally, through an ablation-based procedure, NLICV pinpoints the exact sentences driving the constraint verification, yielding faithful, understandable evidence for its evaluations.

Problem

• LLM personalization
• evaluation paradigms
• interpretability
• semantic constraints
• Natural Language Inference

Innovation

• Natural Language Inference
• Personalization Evaluation
• Semantic Constraints
• Interpretable AI
• Scalable Assessment