Beyond Pointwise Scores: Decomposed Criteria-Based Evaluation of LLM Responses
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912680343764992 |
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| author | Yu, Fangyi Seedat, Nabeel Herrmannova, Dasha Schilder, Frank Schwarz, Jonathan Richard |
| author_facet | Yu, Fangyi Seedat, Nabeel Herrmannova, Dasha Schilder, Frank Schwarz, Jonathan Richard |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16093 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Pointwise Scores: Decomposed Criteria-Based Evaluation of LLM Responses Yu, Fangyi Seedat, Nabeel Herrmannova, Dasha Schilder, Frank Schwarz, Jonathan Richard Computation and Language Artificial Intelligence 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. |
| title | Beyond Pointwise Scores: Decomposed Criteria-Based Evaluation of LLM Responses |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2509.16093 |