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

arXiv: https://arxiv.org/abs/2409.12257 · HTML: https://mlsanity.com/paper/2409.12257/

## 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 (most cited first, 28 of 28)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Locating and Editing Factual Associations in GPT](https://mlsanity.com/paper/2202.05262/) (2022)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021)
- [Mass-Editing Memory in a Transformer](https://mlsanity.com/paper/2210.07229/) (2022)
- [Fast Model Editing at Scale](https://mlsanity.com/paper/2110.11309/) (2021)
- [Memory-Based Model Editing at Scale](https://mlsanity.com/paper/2206.06520/) (2022)
- [MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions](https://mlsanity.com/paper/2305.14795/) (2023)
- [Modifying Memories in Transformer Models](https://mlsanity.com/paper/2012.00363/) (2020)
- [DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text](https://mlsanity.com/paper/2306.05540/) (2023)
- [Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting](https://mlsanity.com/paper/2004.12651/) (2020)
- [RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs](https://mlsanity.com/paper/2010.04029/) (2020)
- [An LLM can Fool Itself: A Prompt-Based Adversarial Attack](https://mlsanity.com/paper/2310.13345/) (2023)
- [AceGPT, Localizing Large Language Models in Arabic](https://mlsanity.com/paper/2309.12053/) (2023)
- [MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learning](https://mlsanity.com/paper/2402.11260/) (2024)
- [Multi-hop Question Answering under Temporal Knowledge Editing](https://mlsanity.com/paper/2404.00492/) (2024)
- [Understanding Forgetting in Continual Learning with Linear Regression](https://mlsanity.com/paper/2405.17583/) (2024)
- [PokeMQA: Programmable knowledge editing for Multi-hop Question Answering](https://mlsanity.com/paper/2312.15194/) (2023)
- [A Hopfieldian View-based Interpretation for Chain-of-Thought Reasoning](https://mlsanity.com/paper/2406.12255/) (2024)
- [Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations](https://mlsanity.com/paper/2004.03554/) (2020)
- [Leveraging Logical Rules in Knowledge Editing: A Cherry on the Top](https://mlsanity.com/paper/2405.15452/) (2024)
- [Dialectical Alignment: Resolving the Tension of 3H and Security Threats of LLMs](https://mlsanity.com/paper/2404.00486/) (2024)
- [Improved Knowledge Graph Embedding using Background Taxonomic Information](https://mlsanity.com/paper/1812.03235/) (2018)
- [Approximating Human-Like Few-shot Learning with GPT-based Compression](https://mlsanity.com/paper/2308.06942/) (2023)
- [GRI: Graph-based Relative Isomorphism of Word Embedding Spaces](https://mlsanity.com/paper/2310.12360/) (2023)
- [GARI: Graph Attention for Relative Isomorphism of Arabic Word Embeddings](https://mlsanity.com/paper/2310.13068/) (2023)

## Built on by (most cited first, 9 of 9)

- [The Landscape of Arabic Large Language Models (ALLMs): A New Era for Arabic Language Technology](https://mlsanity.com/paper/2506.01340/) (2025)
- [Predicting LLM Output Length via Entropy-Guided Representations](https://mlsanity.com/paper/2602.11812/) (2026)
- [COMPKE: Complex Question Answering under Knowledge Editing](https://mlsanity.com/paper/2506.00829/) (2025)
- [The Compositional Architecture of Regret in Large Language Models](https://mlsanity.com/paper/2506.15617/) (2025)
- [Editing Across Languages: A Survey of Multilingual Knowledge Editing](https://mlsanity.com/paper/2505.14393/) (2025)
- [Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images](https://mlsanity.com/paper/2506.07184/) (2025)
- [A-IPO: Adaptive Intent-driven Preference Optimization](https://mlsanity.com/paper/2510.10077/) (2025)
- [ADS-Edit: A Multimodal Knowledge Editing Dataset for Autonomous Driving Systems](https://mlsanity.com/paper/2503.20756/) (2025)
- [An Exploration of Knowledge Editing for Arabic](https://mlsanity.com/paper/2507.09629/) (2025)

## 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.