Boosting large-language models for fact-checking: leveraging verbalized tabular data as evidence

Automated fact-checking (AFC) becomes a crucial task because it helps verify the truthfulness of the claim in written or spoken language to decrease the effects of misleading information on social media. Among various forms of information, tabular information plays an important role in fact-checking, as it is an efficient and compact method for storing and representing complex scenario information in the real world. Although large-language models (LLMs) demonstrate robustness in natural language processing, they struggle with understanding and reasoning in tabular data. Therefore, we propose TabV4FC (Tabular Verbalization for Fact Checking) - a simple framework that integrates the pre-trained language model (TAPEX - Table Pre-training via Learning a Neural SQL Executor) for table-to-text generation with robust LLMs, including Qwen, Llama, and DeepSeek-R1, to leverage the reasoning ability of LLMs on natural language text for solving the fact-checking task on tabular evidence. We also evaluate our proposed method on three different tabular fact-checking datasets. Our empirical results show that the description text generated from the table-to-text model significantly boosts the accuracy of LLMs. In addition, our framework achieves competitive results compared to baselines and other state-of-the-art methods, such as TART and ProTrix, even in zero-shot settings. Finally, from the error analysis, we show that there is room for further improvement of our framework, including improving the ability of table understanding via extracting latent information from the table and advanced prompting methods for LLMs.