Beyond Pointwise Scores: Decomposed Criteria-Based Evaluation of LLM Responses

• URL: http://arxiv.org/abs/2509.16093v2
• Date: Fri, 31 Oct 2025 18:19:46 GMT
• Title: Beyond Pointwise Scores: Decomposed Criteria-Based Evaluation of LLM Responses
• Authors: Fangyi Yu, Nabeel Seedat, Dasha Herrmannova, Frank Schilder, Jonathan Richard Schwarz,
• Abstract summary: DeCE is a decomposed LLM evaluation framework that separates precision (factual accuracy and relevance) and recall (coverage of required concepts)<n>We instantiate DeCE to evaluate different LLMs on a real-world legal QA task involving multi-jurisdictional reasoning and citation grounding.
• Score: 23.308803725940383
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Evaluating long-form answers in high-stakes domains such as law or medicine remains a fundamental challenge. Standard metrics like BLEU and ROUGE fail to capture semantic correctness, and current LLM-based evaluators often reduce nuanced aspects of answer quality into a single undifferentiated score. We introduce DeCE, a decomposed LLM evaluation framework that separates precision (factual accuracy and relevance) and recall (coverage of required concepts), using instance-specific criteria automatically extracted from gold answer requirements. DeCE is model-agnostic and domain-general, requiring no predefined taxonomies or handcrafted rubrics. We instantiate DeCE to evaluate different LLMs on a real-world legal QA task involving multi-jurisdictional reasoning and citation grounding. DeCE achieves substantially stronger correlation with expert judgments ($r=0.78$), compared to traditional metrics ($r=0.12$), pointwise LLM scoring ($r=0.35$), and modern multidimensional evaluators ($r=0.48$). It also reveals interpretable trade-offs: generalist models favor recall, while specialized models favor precision. Importantly, only 11.95% of LLM-generated criteria required expert revision, underscoring DeCE's scalability. DeCE offers an interpretable and actionable LLM evaluation framework in expert domains.

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