3 papers
cs.CL2025
DS@GT at CheckThat! 2025: Evaluating Context and Tokenization Strategies for Numerical Fact Verification
Maximilian Heil, Aleksandar Pramov
Numerical claims, statements involving quantities, comparisons, and temporal references, pose unique challenges for automated fact-checking systems. In this study, we evaluate mode…
cs.CL2025
DS@GT at CheckThat! 2025: Detecting Subjectivity via Transfer-Learning and Corrective Data Augmentation
Maximilian Heil, Dionne Bang
This paper presents our submission to Task 1, Subjectivity Detection, of the CheckThat! Lab at CLEF 2025. We investigate the effectiveness of transfer-learning and stylistic data a…
cs.CV2024
Fine-Grained Classification for Poisonous Fungi Identification with Transfer Learning
Christopher Chiu, Maximilian Heil, Teresa Kim +1
FungiCLEF 2024 addresses the fine-grained visual categorization (FGVC) of fungi species, with a focus on identifying poisonous species. This task is challenging due to the size and…