Table-Text Alignment: Explaining Claim Verification Against Tables in Scientific Papers (2025)

Abstract: 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 cell-level 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.

## introduction claim verification against tables requires models to determine whether a natural language claim is supported or refuted based on structured tabular data. several benchmarks have been proposed in the general domain, such as tabfact @xcite , infotabs @xcite , and feverous @xcite , primarily focusing on wikipedia tables. however, tables in scientific papers pose additional challenges: they are often denser, more structured, and require domainspecific reasoning. two datasets have recently addressed this task in the scientific domain: sem-tab-facts (sem; @xcite , which includes both claim verification and cell-level evidence selection, and sc-itab @xcite , which focuses solely on claim verification. while sem includes an alignment component, its claims are crowd-generated and simplified, limiting their representativeness. scitab, in contrast, uses naturally occurring claims but lacks explicit annotations that explain why a given label is correct. we argue that label prediction alone, as in sc-itab, is not enough. it fails to reveal whether a model truly understands the table content, nor does it provide interpretable reasoning. for both evaluation and practical use, models need to go beyond classification and provide explanations grounded in tabular evidence. from the perspective of scientific reading tools @xcite , table-text alignment is also crucial. it allows readers to quickly locate which parts of a table are referenced in the text, improving comprehension and accelerating the reading process. such alignments could directly support scientific workflows by making tabular evidence more accessible and interpretable. to address these limitations, we reframe table-text alignment as an explanation task for scientific claim verification. specifically, we extend the scitab dataset with human-annotated cell-level rationales. for each claim-table pair, annotators verify the claim label and highlight the minimal set of table cells needed to support the decision. during annotation, we frequently encountered ambiguous cases in claim interpretation and evidence selection. to capture these edge cases systematically, we introduce a taxonomy of five ambiguity types in scientific table-based verification: (i) table conversion errors, (ii) additional context requirements, (iii) unexpected claim types, (iv) subjective adjectives, and (v) unclear claims. we use our dataset to evaluate various types of large language models (llms), including tablebased models, open-source llms, and closedsource llms. our experiments also incorporate three different prompting strategies. on average, our human-annotated cell-level rationales help improve the performance of the claim label prediction task. the results show that while models achieve high macro-f1 scores on the claim label prediction task, their performance on the cell selection task remains low-even for advanced models like gpt-4o. the highest score, 50.8, is achieved by qwen 2.5 72b using cot prompting. further analysis of the correlation between the two tasks reveals that although llms often correctly predict the claim label, their ability to identify the corresponding explanation cells is still limited. ## related work claim verification has been studied across multiple domains, including news @xcite , wikipedia @xcite , scientific literature @xcite , and medicine @xcite . beyond plain text, recent efforts have extended claim verification to structured or multimodal evidence, including tables @xcite , figures @xcite , knowledge graphs @xcite and multimodal data @xcite . among table-based datasets, sem @xcite and tabevidence @xcite are most related to our work. however, sem features simplified, crowd-generated claims, while tabevidence is limited to two-column wikipedia tables, lacking the complexity of scientific tables. recent frameworks like chain-of-table @xcite and dater @xcite include evidence selection steps, but only report label accuracy, without evaluating the relevance or quality of the selected evidence, limiting trust in their predictions. in contrast, our work emphasizes explanation via alignment, explicitly evaluating whether the model selects the correct table cells needed for verification, providing a more faithful and interpretable assessment of reasoning. ## dataset creation in this section, we first briefly introduce the existing scitab dataset, on which our work is based. we then describe the process of obtaining the extended version, scitabalign, with cell-level explanations. finally, we propose a taxonomy of five common ambiguity types, which we hope future work considers to build more reliable claim verification datasets. we note that we remove all ambiguous cases from our dataset, reducing the number of claims from 868 to 372. ## experimental settings models. we use three groups of models in our experiments: table-based llms, open-source llms, and closed-source llms. for tablebased llms, we use tapas-base and tapaslarge @xcite , pretrained for reasoning over tabular input. for open-source llms, we use the instruction-tuned variants of qwen 2.5 (7b and 72b, @xcite and llama 3.1 (8b and 70b, @xcite . for closed-source llms, we use gpt-4o @xcite . prompting strategies. our dataset contains two subtasks: (1) claim label prediction and (2) celllevel evidence selection. we conduct experiments using three prompting strategies: zero-shot, fewshot, and chain-of-thought (cot; @xcite . for few-shot and cot promptings, we use four demonstration examples selected from the revised subset of samples not included in the evaluation set, ensuring fair evaluation. tabular representation. following wang et al. @xcite , who found that the pipe encoding format with explicit tags (e.g. col and row 1) outperformed html, tsv, and markdown formats for tabular data, we adopt pipe encoding for all of our experiments. evaluation metrics. we use macro-f1 to evaluate both tasks in our dataset: (1) claim label prediction and (2) cell-level evidence selection. for the claim label prediction task, we compare the predicted label with the ground-truth label. our dataset contains two labels: supported and refuted. for the cell-level evidence selection task, both ground-truth and predicted evidence are represented as lists of (row, column) index tuples using pipe encoding. for example, (1, 2) refers to the cell in row 1, column 2. we compare the two lists to obtain true positives, false positives, and false negatives, and then calculate precision, recall, and f1 based on these values. the macro-f1 score is computed as the average of the individual f1 scores. ## results all results are shown in ## analyses to better understand the correlation between the claim label prediction task and the cell evidence selection task, we categorize outcomes into four types: correct-correct, correct-incorrect, incorrect-correct, and incorrect-incorrect. for claim label prediction, correctness is easily determined based on whether the predicted label (supported or refuted) matches the ground truth. in contrast, cell evidence selection involves list-based predictions, making exact matches more challenging. therefore, we consider two evaluation criteria: exact match (em) and a relaxed case where an f1 score of 50.0 or higher is considered correct. the percentage distribution of these cases is shown in ## conclusion in this work, we highlighted the limitations of scientific claim verification systems that focus solely on label prediction, arguing for the importance of interpretability through evidence selection. by reframing table-text alignment as an explanation task and introducing a new dataset with humanannotated cell-level rationales, we provide a more rigorous benchmark for evaluating model reasoning. additionally, we proposed a taxonomy of ambiguous cases in claim verification against tables, which can support future work on dataset construction. our findings demonstrate that while llms often predict the correct claim labels, they frequently fail to identify the minimal supporting evidence, revealing a gap between accuracy and faithful reasoning. this underscores the need for future work to prioritize not just correctness, but also alignment with human-understandable rationales in scientific fact verification tasks.