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

TL;DR

This paper introduces MQA-KEAL, a novel multi-hop question answering framework for Arabic that incorporates knowledge editing and external memory, addressing language-specific challenges and providing new benchmarks for evaluation.

Contribution

It proposes a new multi-hop question answering method for Arabic that uses structured knowledge editing and external memory, along with new benchmarks for evaluation.

Findings

MQA-KEAL outperforms baseline models significantly.

Introduces MQUAKE-AR, an Arabic translation of a benchmark.

Develops MQA-AEVAL for evaluating multi-hop QA in Arabic.

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.