BEDAA: Bayesian Enhanced DeBERTa for Uncertainty-Aware Authorship Attribution (2025.findings-acl)
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| Challenge: | Existing methods for authorship attribution struggle with trustworthiness and interpretability across domains, languages, and stylistic variations. |
| Approach: | They propose a Bayesian-Enhanced DeBERTa framework that integrates Bayes' reasoning with transformer-based language models to enable uncertainty-aware authorship attribution. |
| Outcome: | The proposed framework achieves 19.69% improvement in F1-score across multiple authorship attribution tasks, including binary, multiclass, and dynamic authorship detection. |
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