Cross-Lingual Adaptation for Multilingual Table Question Answering and Comparative Evaluation with Large Language Models

Table question answering has been studied using datasets drawn from a variety of tabular sources and task formats. However, most publicly available resources have been created in high-resource languages such as English. For low-resource languages, researchers are often required to construct new datasets or translate existing ones, which incurs substantial time, effort, and financial cost. In contrast to natural language text, table data consists of structured entries whose interpretation is less affected by language-specific syntax or word order. In this work, we present a cost-effective strategy for multilingual table QA that relies on selectively translating only the questions of existing datasets. Leveraging the language-agnostic structure of tables, our approach maintains the original table content while translating queries into multiple target languages. To address possible performance drops caused by using table data in the source language rather than the target language, we apply cross-lingual adaptation techniques using contrastive learning and adversarial training. In addition, to strengthen reasoning ability while avoiding degradation in languages not seen during pre-training, we perform supplementary pre-training of a RoBERTa-based multilingual encoder with SQL-derived table data. Finally, we extend our investigation beyond encoder-based architectures and evaluate decoder-only large language models under the same multilingual table QA setting. The experiments show that LLaMA-3 models exhibit strong cross-lingual generalization even without using translated table context and often achieve competitive performance using only Korean table data. Moreover, the performance gap among training configurations such as translated queries or translated datasets is notably smaller compared to encoder-based models, highlighting the inherent multilingual robustness of modern LLMs. We further evaluate LLaMA-3 models on domain-specific table datasets and observe that domain knowledge acquired from Korean tables transfers effectively across languages even without multilingual supervision, underscoring the potential of LLMs for specialized multilingual table reasoning. These findings demonstrate that LLMs can serve as an effective alternative for multilingual table QA, particularly in low-resource or partially translated environments.