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Automated Text Identification Using CNN and Training Dynamics (2405.11212v1)
Published 18 May 2024 in cs.CL
Abstract: We used Data Maps to model and characterize the AuTexTification dataset. This provides insights about the behaviour of individual samples during training across epochs (training dynamics). We characterized the samples across 3 dimensions: confidence, variability and correctness. This shows the presence of 3 regions: easy-to-learn, ambiguous and hard-to-learn examples. We used a classic CNN architecture and found out that training the model only on a subset of ambiguous examples improves the model's out-of-distribution generalization.
- Overview of autextification at iberlef 2023: Detection and attribution of machine-generated text in multiple domains, in: Procesamiento del Lenguaje Natural, Jaén, Spain, 2023.
- Dataset cartography: Mapping and diagnosing datasets with training dynamics, in: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, Online, 2020, pp. 9275–9293. URL: https://aclanthology.org/2020.emnlp-main.746. doi:10.18653/v1/2020.emnlp-main.746.
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