Arabic WikiTableQA: Benchmarking Question Answering over Arabic Tables Using Large Language Models

Abstract

Table-based question answering (TableQA) has made significant progress in recent years; however, most advancements have focused on English datasets and SQL-based techniques, leaving Arabic TableQA largely unexplored. This gap is especially critical given the widespread use of structured Arabic content in domains such as government, education, and media. The main challenge lies in the absence of benchmark datasets and the difficulty that large language models (LLMs) face when reasoning over long, complex tables in Arabic, due to token limitations and morphological complexity. To address this, we introduce Arabic WikiTableQA, the first large-scale dataset for non-SQL Arabic TableQA, constructed from the WikiTableQuestions dataset and enriched with natural questions and gold-standard answers. We developed three methods to evaluate this dataset: a direct input approach, a sub-table selection strategy using SQL-like filtering, and a knowledge-guided framework that filters the table using semantic graphs. Experimental results with an LLM show that the graph-guided approach outperforms the others, achieving 74% accuracy, compared to 64% for sub-table selection and 45% for direct input, demonstrating its effectiveness in handling long and complex Arabic tables.