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 update and/or edit the LLMs’ prior knowledge and in turn test it via Multi-hop Question Answering (MQA), yet so far these studies are primarily focused and/or developed for 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. We release the codes for MQA-KEAL at https: //github.com/asif6827/MQA-Keal.

Knowledge Editing-Efficiently modifying the knowledge in large language models has gathered great attention. Current benchmarks primarily use multi-hop question answering to assess and analyze newly injected or updated knowledge. However, we argue that these benchmarks fail to effectively evaluate how well the updated models apply this knowledge in real-life scenarios, particularly when questions require complex reasoning involving one-to-many relationships or multi-step logical intersections. To fill in this gap, we introduce a new benchmark, COMPKE: Complex Question Answering under Knowledge Editing, which includes 11,924 complex questions that reflect real-life situations. We perform a comprehensive evaluation of four different knowledge editing methods in COMPKE, and our results show that the performance of these methods varies between different models. For example, MeLLo achieves an accuracy of 39.47 on GPT-4o-mini but drops significantly to 3.83 on Qwen2.5-3B. We further analyze the reasons behind these results from both methodological and model perspectives. Our dataset will be publicly available on GitHub.

Large language models (LLMs) have demonstrated remarkable proficiency in handling a wide range of tasks within the software engineering domain, but their ability to perform code migration—adapting code to different environments—remains underexplored. In this work, we propose a novel benchmark, : Code Migration Across Environment, designed to evaluate LLMs’ performance in handling code migration tasks. The benchmark comprises 922 data points across 19 Python and Java packages, offering three tasks to systematically evaluate code migration: identifying version-incompatible functions, determining function changes, and adapting code to target environments. Experimental evaluation of across seven LLMs revealed an average pass@1 rate of 26.50%, with GPT-4o performing best at 43.84%. We highlight our key findings as follows: (i) LLMs are more familiar with newer function versions, making them better at migrating legacy code, and (ii) a logical inconsistency where LLMs sometimes identify irrelevant function changes for the target migration environment.