  Large language models (LLMs) have emerged as a promising alternative to
expensive human evaluations. However, the alignment and coverage of LLM-based
evaluations are often limited by the scope and potential bias of the evaluation
prompts and criteria. To address this challenge, we propose HD-Eval, a novel
framework that iteratively aligns LLM-based evaluators with human preference
via Hierarchical Criteria Decomposition. HD-Eval inherits the essence from the
evaluation mindset of human experts and enhances the alignment of LLM-based
evaluators by decomposing a given evaluation task into finer-grained criteria,
aggregating them according to estimated human preferences, pruning
insignificant criteria with attribution, and further decomposing significant
criteria. By integrating these steps within an iterative alignment training
process, we obtain a hierarchical decomposition of criteria that
comprehensively captures aspects of natural language at multiple levels of
granularity. Implemented as a white box, the human preference-guided aggregator
is efficient to train and more explainable than relying solely on prompting,
and its independence from model parameters makes it applicable to closed-source
LLMs. Extensive experiments on three evaluation domains demonstrate the
superiority of HD-Eval in further aligning state-of-the-art evaluators and
providing deeper insights into the explanation of evaluation results and the
task itself.
