HD -Eval: Aligning Large Language Model Evaluators Through Hierarchical Criteria Decomposition (2024)

Abstract: 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.

## introduction with the rapid development of llms and rising significance on nlg evaluations, an emerging line of works explores utilizing llm as reference-free text quality evaluators @xcite @xcite . to leverage the instruction following capability of llms, existing works utilize a single piece of criteria (as a prompt) to evaluate a given sample. given * contributed during internship at microsoft. the superior instruction-following capability and immense knowledge obtained through pre-training, llm-based evaluators substantially outperform previous automatic evaluation metrics @xcite , and opens a promising alternative for human evaluation. however, despite their achievements, an emerging line of research questions the alignment and trustworthiness of llm judgments. as recent studies point out, these approaches are limited by the bias of prompt design @xcite , resulting in potential biases in its judgments @xcite , demanding per-task calibration on evaluation prompts to mitigate @xcite . one core limitation of using a single criterion to evaluate text quality is that it may not capture the complexity and diversity of human evaluations and judgments. human thinking is not linear or monolithic, but rather comprehensive and naturally follows a hierarchical order @xcite . when we read a book, we may evaluate it from different perspectives, such as plot, characters, style, and theme, each of which can further be naturally divided into more specific criteria. hierarchical thinking @xcite allows humans to resolve complex problems by first breaking them down into more tangible sub-problems, and then integrating the solutions at different levels of abstraction @xcite . correspondingly, mainstream human evaluation protocols also leverage hierarchical critiques @xcite . our core motivation is to empower the alignment of llm-based evaluators by rooting the evaluation mindset of human experts into design, while also harnessing state-of-the-art generic capabilities of llms. drawing inspirations from the above, we propose hd-eval, a novel framework to align llm-based evaluator towards human preference through hierarchical criteria decomposition. specifically, the design of critical components of hd-eval inherits the essence of the human evaluation mindset: task decomposition, analysis of all sub-tasks, and a final comprehensive evaluation. correspondingly, we propose 3 crucial stages: (1) hierarchical criteria decomposition, where we decompose an evaluation task into a hierarchy of evaluation criteria, each focusing on different evaluation aspects with various granularity; (2) human preference-guided aggregation, where we aggregate evaluation results at each hierarchy to obtain a final judgment, with respect to the estimated preference of human experts on different hierarchies; to align an llm-based evaluator toward human preference, we propose iterative alignment training framework to seamlessly integrate the 3 stages above in a layer-wise iterative fashion. when the training process of hd-eval completes, we obtain a pair of finalized criteria decomposition and human preference-guided aggregator, which could be applied to evaluation samples upon application. we highlight the following key contributions of hd-eval as follows: ## analysis 4.1 case study to investigate the effect of hierarchical criteria decomposition, we present a case study on evaluating natural language conversation. in our experiments, we explore decomposing an nlg evaluation task into a maximum of 3 hierarchies (layers). as illustrated in figure 3 , the highest layer of hd-eval ## conclusion drawing inspiration from human evaluation mindsets, we propose hd-eval, a novel framework that empowers llm-based evaluators through explainable alignment. through criteria decomposition, human preference-guided aggregation, and attribution pruning, the criteria obtained with hd-eval demonstrates a comprehensive focus on different levels of details. extensive experiments on three nlg evaluation tasks demonstrate the effectiveness of hd-eval. detailed analysis shows the efficiency and explainability of hd-eval, and opens up brand new perspectives in understanding preferences of human evaluations.