Reasoning over temporal and numerical data, such as time series, is a crucial aspect of factchecking.While many systems have recently been developed to handle this form of evidence, their evaluation remains limited by existing datasets, which often lack structured evidence, provide insufficient justifications for verdicts, or rely on synthetic claims.In this paper, we introduce TSVER, a new benchmark dataset for fact verification focusing on temporal and numerical reasoning with time-series evidence.TSVER contains 287 real-world claims sourced from 38 fact-checking organizations and a curated database of 400 time series covering diverse domains.Each claim is annotated with time frames across all pertinent time series, along with a verdict and justifications reflecting how the evidence is used to reach the verdict.Using an LLM-assisted multi-step annotation process, we improve the quality of our annotations and achieve an inter-annotator agreement of κ = 0.745 on verdicts.We also develop a baseline for verifying claims against timeseries evidence and show that even the state-ofthe-art reasoning models like Gemini-2.5-Proare challenged by time series, achieving a 63.37 accuracy score on verdicts and an Ev 2 R score of 48.63 on verdict justifications.

Fact verification systems assess a claim's veracity based on evidence.An important consideration in designing them is faithfulness, i.e. generating explanations that accurately reflect the reasoning of the model.Recent works have focused on natural logic, which operates directly on natural language by capturing the semantic relation of spans between an aligned claim with its evidence via set-theoretic operators.However, these approaches rely on substantial resources for training, which are only available for high-resource languages.To this end, we propose to use question answering to predict natural logic operators, taking advantage of the generalization capabilities of instruction-tuned language models.Thus, we obviate the need for annotated training data while still relying on a deterministic inference system.In a few-shot setting on FEVER, our approach outperforms the best baseline by 4.3 accuracy points, including a state-of-the-art pre-trained seq2seq natural logic system, as well as a state-of-the-art prompt-based classifier.Our system demonstrates its robustness and portability, achieving competitive performance on a counterfactual dataset and surpassing all approaches without further annotation on a Danish verification dataset.A human evaluation indicates that our approach produces more plausible proofs with fewer erroneous natural logic operators than previous natural logic-based systems.

Marta Bañón, Pinzhen Chen, Barry Haddow, Kenneth Heafield, Hieu Hoang, Miquel Esplà-Gomis, Mikel L. Forcada, Amir Kamran, Faheem Kirefu, Philipp Koehn, Sergio Ortiz Rojas, Leopoldo Pla Sempere, Gema Ramírez-Sánchez, Elsa Sarrías, Marek Strelec, Brian Thompson, William Waites, Dion Wiggins, Jaume Zaragoza. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020.