Dealing with long and highly complex technical text is a challenge for Large Language Models (LLMs), which still have to unfold their potential in supporting expensive and timeintensive processes like patent drafting. Within patents, the description constitutes more than 90% of the document on average. Yet, its automatic generation remains understudied. When drafting patent applications, patent attorneys typically receive invention reports (IRs), which are usually confidential, hindering research on LLM-supported patent drafting. Often, prepublication research papers serve as IRs. We leverage this duality to build PAP2PAT, an open and realistic benchmark for patent drafting consisting of 1.8k patent-paper pairs describing the same inventions. To address the complex longdocument patent generation task, we propose chunk-based outline-guided generation using the research paper as invention specification. Our extensive evaluation using PAP2PAT and a human case study show that LLMs can effectively leverage information from the paper, but still struggle to provide the necessary level of detail. Fine-tuning leads to more patent-style language, but also to more hallucination. We release our data and code https://github.com/boschresearch/Pap2Pat.

翻译：处理冗长且高度复杂的技术文本对大型语言模型（LLM）而言仍具挑战，其在支持专利撰写这类昂贵且耗时的流程方面尚未充分发挥潜力。在专利文件中，说明书平均占文档篇幅的90%以上，但其自动生成研究仍显不足。专利律师在起草专利申请时通常依赖保密性较高的发明报告（IR），这阻碍了基于LLM的专利撰写研究。预发表的研究论文常可作为IR的替代。我们利用这种双重性构建了PAP2PAT——一个开放且真实的专利撰写基准测试集，包含1.8k组描述相同发明的专利-论文对。针对复杂的长文档专利生成任务，我们提出基于分块的提纲引导生成方法，以研究论文作为发明说明书。通过PAP2PAT的广泛评估及人工案例研究，我们发现LLM能有效利用论文信息，但仍难以提供必要的细节层次。微调虽能增强专利式语言风格，却会导致更多幻觉现象。我们已公开数据集与代码：https://github.com/boschresearch/Pap2Pat。