RePanda: Pandas-powered Tabular Verification and Reasoning

Fact-checking tabular data is essential for ensuring the accuracy of structured information.However, existing methods often rely on blackbox models with opaque reasoning.We introduce RePanda, a structured fact verification approach that translates claims into executable pandas queries, enabling interpretable and verifiable reasoning.To train RePanda, we construct PanTabFact, a structured dataset derived from the TabFact train set, where claims are paired with executable queries generated using DeepSeek-Chat 1 and refined through automated error correction.Fine-tuning DeepSeek-coder-7Binstruct-v1.5 on PanTabFact, RePanda achieves 84.09% accuracy on the TabFact test set.To evaluate Out-of-Distribution (OOD) generalization, we interpret question-answer pairs from WikiTableQuestions as factual claims and refer to this dataset as WikiFact.Without additional fine-tuning, RePanda achieves 84.72% accuracy on WikiFact, significantly outperforming all other baselines and demonstrating strong OOD robustness.Notably, these results closely match the zero-shot performance of DeepSeek-Chat (671B), indicating that our fine-tuning approach effectively distills structured reasoning from a much larger model into a compact, locally executable 7B model.PanTab-Fact is publically available on HuggingFace at datasets/AtoosaChegini/PanTabFact.Beyond fact verification, RePanda extends to tabular question answering by generating executable queries that retrieve precise answers.To support this, we introduce Pan-Wiki, a dataset mapping WikiTableQuestions to pandas queries.Fine-tuning on PanWiki, RePanda achieves 75.1% accuracy in direct answer retrieval.These results highlight the