Domain-Specific LLM Evaluation: Why Generic Rubrics Fall Short

In high-stakes fields like law, medicine, and finance, evaluating language models (LLMs) using generic metrics such as BLEU and ROUGE falls short because these metrics focus on linguistic similarity rather than the domain-specific requirements of factual accuracy, regulatory compliance, and reliable reasoning. This inadequacy can lead to significant risks, such as legal liability or clinical harm, when AI outputs are incorrectly deemed high-quality. To address this, domain-specific LLM evaluation is recommended, which involves assessing AI outputs against criteria meaningful within a specific field, using expert annotations to establish ground truth and improve automated evaluations. This approach requires decomposing quality into independently assessable dimensions and using a combination of expert human review, LLM judges, and automated metrics to create a robust evaluation framework that can adapt to specific domain obligations and ensure safety and compliance. As regulatory mandates increasingly require domain-expert eval loops, integrating this multi-layered evaluation strategy becomes crucial for AI deployment in specialized contexts, enhancing both accuracy and trust in AI systems.

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