Evaluating Style-Personalized Text Generation: Challenges and Directions

2025-08-08

AI Summary

Existing automatic evaluation metrics (e.g., BLEU, ROUGE) inadequately capture stylistic fidelity and author identifiability in low-resource author-style personalized text generation. Method: We construct a multi-dimensional evaluation benchmark comprising three task categories—domain discrimination, author attribution, and personalized generation discrimination—and systematically assess conventional metrics while introducing novel paradigms, including style embedding distance and LLM-as-judge evaluation. We further propose a metric fusion strategy to integrate complementary signals. Contribution/Results: Evaluated across eight writing tasks, our integrated framework significantly improves assessment reliability and inter-metric consistency. It establishes the first reproducible, multi-faceted, and style-centric evaluation standard for author-style personalization—grounded in the intrinsic properties of stylistic representation rather than surface-level n-gram overlap.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Machine Learning: Evaluation and Analysis
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

While prior research has built tools and benchmarks towards style personalized text generation, there has been limited exploration of evaluation in low-resource author style personalized text generation space. Through this work, we question the effectiveness of the widely adopted evaluation metrics like BLEU and ROUGE, and explore other evaluation paradigms such as style embeddings and LLM-as-judge to holistically evaluate the style personalized text generation task. We evaluate these metrics and their ensembles using our style discrimination benchmark, that spans eight writing tasks, and evaluates across three settings, domain discrimination, authorship attribution, and LLM personalized vs non-personalized discrimination. We provide conclusive evidence to adopt ensemble of diverse evaluation metrics to effectively evaluate style personalized text generation.

Problem

• Evaluating style-personalized text generation effectively
• Challenges of current metrics like BLEU and ROUGE
• Exploring new evaluation paradigms for low-resource settings

Innovation

• Using style embeddings for evaluation
• Employing LLM-as-judge paradigm
• Adopting ensemble of diverse metrics