Interpretable Predictability-Based AI Text Detection: A Replication Study
Quick summary
arXiv:2603.15034v2 Announce Type: replace-cross Abstract: This paper replicates and extends the system used in the AuTexTification shared task for authorship attribution of machine-generated texts. Exact replication was not possible because of differences in data splits, model availability, and implementation details, which we document as a case study in reproducibility. We tested newer multilingual language models (mDeBERTa-v3-base, Qwen, mGPT) and added 26 document-level stylometric features, using ablation, permutation importance, and SHAP analysis to assess feature influence. A single shar
Key takeaways
- arXiv:2603.15034v2 Announce Type: replace-cross Abstract: This paper replicates and extends the system used in the AuTexTification shared task for authorship attribution of machine-generated texts.
- Exact replication was not possible because of differences in data splits, model availability, and implementation details, which we document as a case study in reproducibility.
- We tested newer multilingual language models (mDeBERTa-v3-base, Qwen, mGPT) and added 26 document-level stylometric features, using ablation, permutation importance, and SHAP analysis to assess feature influence.
Why it matters
The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.
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