Hallucination in Large Language Models (LLMs) remains a significant challenge and continues to draw substantial research attention. The problem becomes especially critical when hallucinations arise in sensitive domains, such as religious discourse. To address this gap, we introduce IslamicEval 2025—the first shared task specifically focused on evaluating and detecting hallucinations in Islamic content. The task consists of two subtasks: (1) Hallucination Detection and Correction of quoted verses (Ayahs) from the Holy Quran and quoted Hadiths; and (2) Qur’an and Hadith Question Answering, which assesses retrieval models and LLMs by requiring answers to be retrieved from grounded, authoritative sources. Thirteen teams participated in the final phase of the shared task, employing a range of pipelines and frameworks. Their diverse approaches underscore both the complexity of the task and the importance of effectively managing hallucinations in Islamic discourse.

We present two comparable diachronic corpora of scientific English and German from the Late Modern Period (17th c.–19th c.) annotated with Universal Dependencies. We describe several steps of data pre-processing and evaluate the resulting parsing accuracy showing how our pre-processing steps significantly improve output quality. As a sanity check for the representativity of our data, we conduct a case study comparing previously gained insights on grammatical change in the scientific genre with our data. Our results reflect the often reported trend of English scientific discourse towards heavy noun phrases and a simplification of the sentence structure (Halliday, 1988; Halliday and Martin, 1993; Biber and Gray, 2011; Biber and Gray, 2016). We also show that this trend applies to German scientific discourse as well. The presented corpora are valuable resources suitable for the contrastive analysis of syntactic diachronic change in the scientific genre between 1650 and 1900. The presented pre-processing procedures and their evaluations are applicable to other languages and can be useful for a variety of Natural Language Processing tasks such as syntactic parsing.