发表机构
School of Computer Science and Technology, University of Science and Technology of China; University of Science and Technology of China; State Key Laboratory of Cognitive Intelligence; CCCC Second Highway Consultants Co., Ltd.（中国科学技术大学计算机科学与技术学院; 中国科学技术大学; 认知智能全国重点实验室; 中交第二公路勘察设计研究院有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出CPSE框架，通过从少量标注文档中校准抽取提示和字段语义，在保持结构契约的同时提升科学PDF抽取性能，实验显示比基线提高9.93个百分点。
AI 中文摘要
一个设计良好的抽取模式并不一定适合可靠的LLM执行。当仅有少量经过验证的抽取结果可用时，通过试错手动调整数百个字段定义代价高昂。我们将此问题定义为少样本模式校准：从少量标注文档中调整现有模式的操作语义，同时保留其结构契约。我们提出了CPSE，一个契约保持的语义抽取框架，它从少量金标准标注中联合校准抽取提示和字段级语义描述。CPSE将模式分解为不变的结构契约和可变的字段语义，并使用基于显式条件的解析将身份发现与记录完成分离。在专家标注的聚合物科学文档上，CPSE比执行匹配的基线提高了9.93个百分点，在独立评判者和盲审专家审计中均获得一致提升。这些结果表明，CPSE能够在保持下游所需输出结构的同时实现低资源模式执行。
英文摘要
A well-designed extraction schema is not necessarily ready for reliable LLM execution. When only limited verified extractions are available, manually tuning hundreds of field definitions through trial and error is costly. We frame this problem as few-instance schema calibration: adapting the operational semantics of an existing schema from a few annotated documents while preserving its structural contract. We introduce CPSE, a contract-preserving semantic extraction framework that jointly calibrates extraction prompts and field-level semantic descriptions from a few gold annotations. CPSE decomposes the schema into an invariant structural contract and mutable field semantics, and further separates identity discovery from record completion using manifest-conditioned resolution. On expert-annotated polymer-science documents, CPSE improves extraction by 9.93 points over an execution-matched baseline, with consistent gains under an independent judge and in a blinded expert audit. These results show that CPSE enables low-resource schema execution while preserving the output structure required downstream.
