MQA-KEAL: Multi-hop Question Answering under Knowledge Editing for Arabic Language
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Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 28 of its 40 references that are in the map. First posted on arXiv 18 Sep 2024.
Builds on
28 of 40 references.
References · 28
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16 more
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Built on by
Citing papers per year: 2025: 8; 2026: 1.
9 papers.
- 2025The Landscape of Arabic Large Language Models (ALLMs): A New Era for Arabic Language Technologycited by 12
- 2026Predicting LLM Output Length via Entropy-Guided Representationscited by 9
- 2025COMPKE: Complex Question Answering under Knowledge Editingcited by 6
- 2025The Compositional Architecture of Regret in Large Language Modelscited by 3
- 2025Editing Across Languages: A Survey of Multilingual Knowledge Editingcited by 2
- 2025Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Imagescited by 1
- 2025A-IPO: Adaptive Intent-driven Preference Optimizationcited by 1
- 2025ADS-Edit: A Multimodal Knowledge Editing Dataset for Autonomous Driving Systemscited by 0
- 2025An Exploration of Knowledge Editing for Arabiccited by 0
- 2026Predicting LLM Output Length via Entropy-Guided Representations12 Feb 2026
- 2025A-IPO: Adaptive Intent-driven Preference Optimization11 Oct 2025
- 2025An Exploration of Knowledge Editing for Arabic13 Jul 2025
- 2025The Compositional Architecture of Regret in Large Language Models18 Jun 2025
- 2025Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images8 Jun 2025
- 2025The Landscape of Arabic Large Language Models (ALLMs): A New Era for Arabic Language Technology2 Jun 2025
- 2025COMPKE: Complex Question Answering under Knowledge Editing1 Jun 2025
- 2025Editing Across Languages: A Survey of Multilingual Knowledge Editing20 May 2025
- 2025ADS-Edit: A Multimodal Knowledge Editing Dataset for Autonomous Driving Systems26 Mar 2025