Pushing the Frontiers of Scientific Fact-Checking: The SCINLP Dataset

AbstractLarge Language Models (LLMs) are increasingly being used to understand how scientific research evolves, drawing growing interest from the research community. However, limited work has explored the scientific fact-checking of research questions and claims from manuscripts, particularly within the NLP domain, an emerging direction for advancing scientific integrity and knowledge validation. In this work, we propose a novel scientific fact-checking dataset, SCINLP, tailored to the NLP domain. Our proposed framework on SCINLP systematically verifies the veracity of complex scientific research questions across varying rationale contexts, while also assessing their temporal positioning. SCINLP includes supporting and refuting research questions from a curated collection of influential and reputable NLP papers published between 2000 and 2024. In our framework, we use multiple LLMs and diverse rationale contexts from our dataset to examine scientific claims and research focus, complemented by feasibility judgments for deeper insight into scientific reasoning in NLP.