# MQA-KEAL: Multi-hop Question Answering under Knowledge Editing for Arabic Language

Source: [MQA-KEAL: Multi-hop Question Answering under Knowledge Editing for Arabic Language](<https://www.sota2.com/research/paper/68fa74d91e209131cea64266>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:19:27.756Z

Authors: Muhammad Asif Ali, Nawal Daftardar, Mutayyaba Waheed, Jianbin Qin, Di Wang

Published: 2024-09-18T18:40:02.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2409.12257>)

[PDF](<https://arxiv.org/pdf/2409.12257.pdf>)