Evaluating the quality of long-form AI-generated content remains a significant
challenge, particularly in achieving consistent alignment with human judgment
across diverse formats. This paper presents the Human-Aligned Long-Form Evaluation
(HALF-Eval) framework, a generalize, scalable and systematic methodology
for assessing the quality of AI-generated long form contents e.g. articles, blogs,
and essays. HALF-Eval utilizes a structured checklist-based evaluation to capture
essential dimensions of content quality, including depth, coherence, relevance, and
evidence support. By leveraging human-annotated data, the framework trains machine
learning models to aggregate individual checklist scores into comprehensive
quality assessments, enabling automated and reliable classification of content as
high- or low-quality. Experimental results demonstrate that HALF-Eval outperforms
conventional LLM-based scoring approaches, achieving closer alignment
with human evaluators and providing actionable feedback for iterative content
improvement. The proposed framework offers a robust foundation for advancing
grounded, human-centric evaluation systems and supports the scalable generation
of high-quality AI-driven long-form content.