PetroBench: A Benchmark for Large Language Models in Petroleum Engineering
Large Language Models are increasingly applied in the petroleum industry, highlighting the need for a domain-specific evaluation framework. This study develops a benchmark for LLMs in petroleum engineering, including a three-stage process of data preprocessing, quality filtering, and multi-model validation. Using expert review, a standardized question bank with strong domain relevance and discriminative capability was constructed. The benchmark covers production, reservoir, and drilling engineering, with 1,200 questions across multiple-choice, true or false, term definition, and short-answer formats. Eight mainstream LLMs were evaluated under a unified API environment. Results show that m...
| Authors | Xiang Wang, Tingting Zhang, Sen Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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