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| <h1 class="title is-1 publication-title">IXAM: Interactive Explainability for Authorship Attribution Models</h1> |
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| <a href="" target="_blank">Milad Alshomary</a><sup>1</sup>,</span> |
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| <a href="" target="_blank">Anisha Bhatnagar</a><sup>2</sup>,</span> |
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| <a href="" target="_blank">Peter Zeng</a><sup>2</sup>, |
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| <a href="" target="_blank">Smaranda Muresan</a><sup>2</sup>, |
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| <a href="" target="_blank">Owen Rambow</a><sup>2</sup>, |
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| <a href="" target="_blank">Kathleen McKeown</a><sup>1,2</sup>, |
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| <span class="author-block"><sup>1</sup>Columbia University, New York, USA</span> |
| <span class="author-block"><sup>2</sup>Stony Brook University, New York, USA</span> |
| <span class="author-block"><sup>2</sup>University of Pennsylvania, Philadelphia, USA</span> |
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| <h2 class="subtitle has-text-centered"> |
| <span class="dnerf">IXAM</span> lets you look into the latent space of authorship attribution models |
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| <h2 class="title is-3">Abstract</h2> |
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| 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. |
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| <h2 class="title">BibTeX</h2> |
| <pre><code>@article{alshomary2025IXAM, |
| title={XAM: Interactive Explainability for Authorship Attribution Models}, |
| author={Alshomary, Milad and Bhatnagar, Anisha and Zeng, Peter and Muresan, Smaranda and Rambow, Owen and McKeown, Kathleen}, |
| journal={arXiv preprint arXiv:2512.06924}, |
| year={2025} |
| }</code></pre> |
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