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arXiv · 2506.20274

Enterprise Large Language Model Evaluation Benchmark

Liya Wang·David Yi·Damien Jose·John Passarelli·James Gao·Jordan Leventis·Kang Li
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Abstract

Large Language Models (LLMs) ) have demonstrated promise in boosting productivity across AI-powered tools, yet existing benchmarks like Massive Multitask Language Understanding (MMLU) inadequately assess enterprise-specific task complexities. We propose a 14-task framework grounded in Bloom's Taxonomy to holistically evaluate LLM capabilities in enterprise contexts. To address challenges of noisy data and costly annotation, we develop a scalable pipeline combining LLM-as-a-Labeler, LLM-as-a-Judge, and corrective retrieval-augmented generation (CRAG), curating a robust 9,700-sample benchmark. Evaluation of six leading models shows open-source contenders like DeepSeek R1 rival proprietary models in reasoning tasks but lag in judgment-based scenarios, likely due to overthinking. Our benchmark reveals critical enterprise performance gaps and offers actionable insights for model optimization. This work provides enterprises a blueprint for tailored evaluations and advances practical LLM deployment.

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Liya Wang, David Yi, Damien Jose, John Passarelli, James Gao, Jordan Leventis, Kang Li. 2025-06-25. Enterprise Large Language Model Evaluation Benchmark. https://arxiv.org/abs/2506.20274

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