Table-Text Alignment: Explaining Claim Verification Against Tables in Scientific Papers
Xanh Ho, Sunisth Kumar, Yun-Ang Wu, Florian Boudin, Atsuhiro Takasu, Akiko Aizawa · 2025
Scientific claim verification against tables typically requires predicting whether a claim is supported or refuted given a table.However, we argue that predicting the final label alone is insufficient: it reveals little about the model's reasoning and offers limited interpretability.To address this, we reframe table-text alignment as an explanation task, requiring models to identify the table cells essential for claim verification.We build a new dataset by extending the SciTab benchmark with human-annotated celllevel rationales.Annotators verify the claim label and highlight the minimal set of cells needed to support their decision.After the annotation process, we utilize the collected information and propose a taxonomy for handling ambiguous cases.Our experiments show that (i) incorporating table alignment information improves claim verification performance, and (ii) most LLMs, while often predicting correct labels, fail to recover human-aligned rationales, suggesting that their predictions do not stem from faithful reasoning.1