Papers by Marek Strong
Zero-Shot Fact Verification via Natural Logic and Large Language Models (2024.findings-emnlp)
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| Challenge: | Recent advancements in fact verification systems with natural logic have enhanced their explainability by aligning claims with evidence through set-theoretic operators, providing faithful justifications. |
| Approach: | They propose a method that utilizes the generalization capabilities of instruction-tuned large language models to provide faithful justifications. |
| Outcome: | The proposed method outperforms other systems that were not specifically trained on natural logic data, and achieves an average accuracy improvement of 8.96 points over the baseline. |
QA-NatVer: Question Answering for Natural Logic-based Fact Verification (2023.emnlp-main)
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| Challenge: | Recent work has focused on natural logic, which operates directly on natural language by capturing the semantic relation of spans between an aligned claim and its evidence via set-theoretic operators. |
| Approach: | They propose to use question answering to predict natural logic operators using generalization capabilities of instruction-tuned language models. |
| Outcome: | The proposed approach outperforms the best baseline on a Danish verification dataset by 4.3 accuracy points. |
TSVer: A Benchmark for Fact Verification Against Time-Series Evidence (2025.emnlp-main)
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| Challenge: | Existing systems for fact-checking lack structured evidence, provide insufficient justifications for verdicts, or rely on synthetic claims. |
| Approach: | They propose a temporal and numerical reasoning dataset based on time-series evidence that is annotated with time frames and a verdict and justifications reflecting how the evidence is used to reach the verdict. |
| Outcome: | The proposed dataset improves the quality of the annotations and achieves an inter-annotator agreement of = 0.745 on verdicts. |