Single and Multi-Hop Question-Answering Datasets for Reticular Chemistry with GPT-4-Turbo
<|reference_start|>Single and Multi-Hop Question-Answering Datasets for Reticular Chemistry with GPT-4-Turbo: 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 literature containing about 2,530 research papers from publishers including NAS, ACS, RSC, Elsevier, and Nature Publishing Group, among others. The dataset has been generated using OpenAI's GPT-4 Turbo, a cutting-edge model known for its exceptional language understanding and generation capabilities. In addition to the Q&A dataset, we also release a dataset of synthesis conditions extracted from the corpus of literature used in this study. The aim of RetChemQA is to provide a robust platform for the development and evaluation of advanced machine learning algorithms, particularly for the reticular chemistry community. The dataset is structured to reflect the complexities and nuances of real-world scientific discourse, thereby enabling nuanced performance assessments across a variety of tasks. The dataset is available at the following link: https://github.com/nakulrampal/RetChemQA<|reference_end|>
@article{rampal2024single,
    title={Single and Multi-Hop Question-Answering Datasets for Reticular Chemistry
  with GPT-4-Turbo},
    author={Nakul Rampal, Kaiyu Wang, Matthew Burigana, Lingxiang Hou, Juri
  Al-Johani, Anna Sackmann, Hanan S. Murayshid, Walaa Abdullah Al-Sumari, Arwa
  M. Al-Abdulkarim, Nahla Eid Al-Hazmi, Majed O. Al-Awad, Christian Borgs,
  Jennifer T. Chayes, Omar M. Yaghi},
    journal={arXiv preprint arXiv:2405.02128},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.02128},
    primaryClass={cs.CL cond-mat.mtrl-sci}
}