Single and Multi-Hop Question-Answering Datasets for Reticular Chemistry with GPT-4-Turbo

arXiv:2405.02128v1 Announce Type: new
Abstract: The rapid advancement in artificial intelligence and natural language processing has led to the development of large-scale datasets aimed at benchmarking the performance of machine learning models. Herein, we introduce 'RetChemQA,' a comprehensive benchmark dataset designed to evaluate the capabilities of such models in the domain of reticular chemistry. This dataset includes both single-hop and multi-hop question-answer pairs, encompassing approximately 45,000 Q&As for each type. The questions have been extracted from an extensive corpus of …

arxiv chemistry cond-mat.mtrl-sci cs.cl datasets gpt gpt-4 gpt-4-turbo question turbo type