Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents
Large language models (LLMs) increasingly support complex professional tasks, yet their capabilities in rule-intensive document review remain insufficiently evaluated. National standard documents, such as China GB/T standards, offer a representative testbed: they are lengthy, highly structured, and governed by explicit rules for scope, terminology, normative wording, and cross-section consistency. Existing benchmarks focus on domain knowledge and question answering, largely overlooking intrinsic quality review for professional documents. Such reviews rely heavily on human experts, making them costly and difficult to scale. To bridge this gap, we introduce GB/T-Bench, the first benchmark f...
| Authors | Tao Wang, Qihao Yang, Rongjiao Liang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 6 August 2026 |
| Last updated at source | 6 August 2026 |
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