mlsanityData to 14 Oct 2026

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

arXiv 2409.12257 ↗First posted 18 Sep 2024cs.CLcited by 9

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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

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  3. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,418
  4. 2022Locating and Editing Factual Associations in GPTcited by 2,691
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  6. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,216
  7. 2022Mass-Editing Memory in a Transformercited by 1,001
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  10. 2023MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questionscited by 300
  11. 2020Modifying Memories in Transformer Modelscited by 224
  12. 2023DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Textcited by 210
16 more
  1. 2020Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgettingcited by 170
  2. 2020RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphscited by 118
  3. 2023An LLM can Fool Itself: A Prompt-Based Adversarial Attackcited by 116
  4. 2023AceGPT, Localizing Large Language Models in Arabiccited by 75
  5. 2024MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learningcited by 63
  6. 2024Multi-hop Question Answering under Temporal Knowledge Editingcited by 36
  7. 2024Understanding Forgetting in Continual Learning with Linear Regressioncited by 30
  8. 2023PokeMQA: Programmable knowledge editing for Multi-hop Question Answeringcited by 23
  9. 2024A Hopfieldian View-based Interpretation for Chain-of-Thought Reasoningcited by 15
  10. 2020Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representationscited by 12
  11. 2024Leveraging Logical Rules in Knowledge Editing: A Cherry on the Topcited by 12
  12. 2024Dialectical Alignment: Resolving the Tension of 3H and Security Threats of LLMscited by 11
  13. 2018Improved Knowledge Graph Embedding using Background Taxonomic Informationcited by 8
  14. 2023Approximating Human-Like Few-shot Learning with GPT-based Compressioncited by 7
  15. 2023GRI: Graph-based Relative Isomorphism of Word Embedding Spacescited by 3
  16. 2023GARI: Graph Attention for Relative Isomorphism of Arabic Word Embeddingscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 8; 2026: 1.

9 papers.