BEDAA: Bayesian Enhanced DeBERTa for Uncertainty-Aware Authorship Attribution

Authorship Attribution (AA) seeks to identify the author of a given text, yet existing methods often struggle with trustworthiness and interpretability, particularly across different domains, languages, and stylistic variations. These challenges arise from the absence of uncertainty quantification and the inability of current models to adapt to diverse authorship tasks. To address these limitations, we introduce BE-DAA, a Bayesian-Enhanced DeBERTa framework that incorporates approximate Bayesian reasoning using Monte Carlo Dropout to enable uncertainty-aware and interpretable authorship attribution. BEDAA achieves up to 19.69% improvement in F1-score across multiple authorship attribution tasks, including binary, multiclass, and dynamic authorship detection. By incorporating confidence ranking, uncertainty decomposition, and probabilistic inference, BEDAA improves robustness while offering transparent decision-making processes. Furthermore, BEDAA extends beyond traditional AA by demonstrating its effectiveness in human vs. machine-generated text classification, code authorship detection, and crosslingual attribution. These advances establish BEDAA as a generalised, interpretable, and adaptable framework for modern authorship attribution challenges.