Human-aligned long-form evaluation (HALF-Eval): Framework for assessing AI-generated content and improvement

Evaluating long-form AI-generated content remains challenging due to the lack of standardized methodologies that robustly align with human judgment across formats such as articles, blogs, and essays. We introduce HALF-Eval, a scalable framework that combines structured, checklist-based evaluation with machine learning aggregation to assess key quality dimensions, including creativity, impact, coherence and relevance. Our approach leverages regression models trained on human-annotated data to synthesize checklist scores into holistic quality classifications, enabling automated yet human-aligned assessments. Experimental results demonstrate that HALF-Eval improves the quality of generated articles by 16% and blogs by 13%, while generalizing effectively to essays. The framework delivers actionable feedback for content refinement and maintains interpretability through its checklist structure. HALFEval advances human-centric evaluation systems and offers a robust foundation for scalable quality control in AI-generated long-form content.