ContractScrub基准为评估合同审查能力提供了首个标准，揭示了前沿模型在特定任务上的局限性，值得法律和AI领域专业人士关注。
合同审查是法律工作中依赖大量文本处理的领域之一，ContractScrub作为首个评估合同审查能力的基准，包含多种错误类型的合同，如定义术语误用、不正确的引用和不一致的语言。前沿模型在相关基准上表现出色，但在ContractScrub上表现不佳，仅有一个模型达到0.75的宏观平均召回率，突显了当前模型的实际限制和特定领域基准的重要性。
ContractScrub: A benchmark for final review of legal contracts
Legal work, with its heavy reliance on processing large amounts of text, is often considered one of the domains most exposed to the use of LLMs. Contract ``scrubbing,'' the final review of transactional agreements for errors and inconsistencies, is a particularly suitable task for automation, because it is routine, painstaking work requiring detailed attention to long documents. Scrubbing also seems to align naturally with the general capabilities expected of frontier LLMs around long-context reasoning, consistency checking, and named entity recognition (NER). Despite the economic value and potential for automation, no formal evaluations of LLMs performing contract scrubbing have been conducted. We introduce ContractScrub, the first benchmark designed to evaluate contract scrubbing capabilities, comprising contracts hand-crafted by experienced lawyers over diverse error categories such as misuse of defined terms, incorrect references, and inconsistent language. Frontier models perform surprisingly poorly with only one model reaching 0.75 macro average recall despite strong performance on seemingly related general benchmarks, demonstrating the practical limits of current models and the importance of narrowly targeted, domain-specific benchmarks for measuring real-world impact.