@inproceedings{knappich-etal-2025-pap2pat,
    title = "{PAP}2{PAT}: Benchmarking Outline-Guided Long-Text Patent Generation with Patent-Paper Pairs",
    author = {Knappich, Valentin  and
      H{\"a}tty, Anna  and
      Razniewski, Simon  and
      Friedrich, Annemarie},
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.496/",
    pages = "9524--9554",
    ISBN = "979-8-89176-256-5",
    abstract = "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 time intensive 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, pre-publication 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 long-document 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."
}