Agent-Based Modelling (ABM) relies on clear documentation to ensure credibility and transparency. Although standards exist for documenting models (e.g. ODD), processes (e.g. TRACE, EABSS), and data use (e.g. RAT-RS), their adoption remains limited due to the effort required to produce documentation that is often treated as supplementary. This paper explores the use of Large Language Models (LLMs) to facilitate and partially automate such processes. We conduct a feasibility study focusing on the underused Rigour and Transparency Reporting Standard (RAT-RS), using four LLMs to extract reports from a published ABM paper. We assess consistency and performance across question types, finding that LLMs generate coherent outputs and perform more reliably on descriptive than on explanatory or evaluative tasks. While LLMs can improve reporting quality and consistency, they also exhibit notable limitations. We identify practical heuristics for when LLM-assisted documentation is reliable and when human oversight is needed and call for systematic community-level exploration to enhance rigour and adoption in ABM reporting.

翻译：基于智能体建模（ABM）依赖清晰的文档来确保可信度与透明度。尽管存在模型文档化标准（如ODD）、过程文档化标准（如TRACE、EABSS）以及数据使用标准（如RAT-RS），但由于生成文档的工作量较大，且这些文档常被视为辅助性内容，其采纳程度仍然有限。本文探索利用大型语言模型（LLMs）来促进并部分自动化此类过程。我们围绕未被充分使用的严谨性与透明度报告标准（RAT-RS）开展可行性研究，使用四种LLMs从一篇已发表的ABM论文中提取报告。我们评估了不同问题类型下的一致性及性能表现，发现LLMs能生成连贯的输出，且在描述性任务中比解释性或评估性任务表现更可靠。虽然LLMs能提高报告质量与一致性，但它们也表现出显著局限性。我们识别出何时LLM辅助的文档化可靠、何时需要人工监督的实用启发式规则，并呼吁开展系统性社区级探索，以提升ABM报告中的严谨性与采纳率。