Scientific rigour tends to be sidelined in favour of bold statements, leading authors to overstate claims beyond what their results support. We present RIGOURATE, a two-stage multimodal framework that retrieves supporting evidence from a paper's body and assigns each claim an overstatement score. The framework consists of a dataset of over 10K claim-evidence sets from ICLR and NeurIPS papers, annotated using eight LLMs, with overstatement scores calibrated using peer-review comments and validated through human evaluation. It employes a fine-tuned reranker for evidence retrieval and a fine-tuned model to predict overstatement scores with justification. Compared to strong baselines, RIGOURATE enables improved evidence retrieval and overstatement detection. Overall, our work operationalises evidential proportionality and supports clearer, more transparent scientific communication.

翻译：科学严谨性常因追求大胆论断而被忽视，导致作者夸大其研究结果所支持的结论。本文提出RIGOURATE——一个两阶段多模态框架，该框架通过检索论文正文中的支撑证据，并为每个声明分配夸大评分。该框架构建了包含超过1万组声明-证据对的数据集（源自ICLR和NeurIPS会议论文），采用八种大型语言模型进行标注，其夸大评分通过同行评审意见校准并经人工评估验证。框架采用微调的重排序器进行证据检索，并利用微调模型预测带有论证依据的夸大评分。与强基线方法相比，RIGOURATE在证据检索和夸大检测方面均表现出显著提升。总体而言，本研究实现了证据比例性的可操作化，为更清晰、更透明的科学交流提供了支持。