{"ID":2832076,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.06924","arxiv_id":"2512.06924","title":"XAM: Interactive Explainability for Authorship Attribution Models","abstract":"We present IXAM, an Interactive eXplainability framework for Authorship Attribution Models. Given an authorship attribution (AA) task and an embedding-based AA model, our tool enables users to interactively explore the model's embedding space and construct an explanation of the model's prediction as a set of writing style features at different levels of granularity. Through a user evaluation, we demonstrate the value of our framework compared to predefined stylistic explanations.","short_abstract":"We present IXAM, an Interactive eXplainability framework for Authorship Attribution Models. Given an authorship attribution (AA) task and an embedding-based AA model, our tool enables users to interactively explore the model's embedding space and construct an explanation of the model's prediction as a set of writing st...","url_abs":"https://arxiv.org/abs/2512.06924","url_pdf":"https://arxiv.org/pdf/2512.06924v1","authors":"[\"Milad Alshomary\",\"Anisha Bhatnagar\",\"Peter Zeng\",\"Smaranda Muresan\",\"Owen Rambow\",\"Kathleen McKeown\"]","published":"2025-12-07T17:07:12Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[]","has_code":false}