MQA-KEAL: Multi-hop Question Answering under Knowledge Editing for Arabic Language
<|reference_start|>MQA-KEAL: Multi-hop Question Answering under Knowledge Editing for Arabic Language: Large Language Models (LLMs) have demonstrated significant capabilities across numerous application domains. A key challenge is to keep these models updated with latest available information, which limits the true potential of these models for the end-applications. Although, there have been numerous attempts for LLMs Knowledge Editing (KE), i.e., to edit the LLMs prior knowledge and in turn test it via Multi-hop Question Answering (MQA), yet so far these studies are primarily focused on English language. To bridge this gap, in this paper we propose: Multi-hop Questioning Answering under Knowledge Editing for Arabic Language (MQA-KEAL). MQA-KEAL stores knowledge edits as structured knowledge units in the external memory. In order to solve multi-hop question, it first uses task-decomposition to decompose the question into smaller sub-problems. Later for each sub-problem, it iteratively queries the external memory and/or target LLM in order to generate the final response. In addition, we also contribute MQUAKE-AR (Arabic translation of English benchmark MQUAKE), as well as a new benchmark MQA-AEVAL for rigorous performance evaluation of MQA under KE for Arabic language. Experimentation evaluation reveals MQA-KEAL outperforms the baseline models by a significant margin.<|reference_end|>
@article{ali2024mqa-keal:,
    title={MQA-KEAL: Multi-hop Question Answering under Knowledge Editing for
  Arabic Language},
    author={Muhammad Asif Ali, Nawal Daftardar, Mutayyaba Waheed, Jianbin Qin and
  Di Wang},
    journal={arXiv preprint arXiv:2409.12257},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12257},
    primaryClass={cs.CL}
}