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<title>IXAM: Interactive Explainability for Authorship Attribution Models</title>
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<h1 class="title is-1 publication-title">IXAM: Interactive Explainability for Authorship Attribution Models</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="" target="_blank">Milad Alshomary</a><sup>1</sup>,</span>
<span class="author-block">
<a href="" target="_blank">Anisha Bhatnagar</a><sup>2</sup>,</span>
<span class="author-block">
<a href="" target="_blank">Peter Zeng</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="" target="_blank">Smaranda Muresan</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="" target="_blank">Owen Rambow</a><sup>2</sup>,
</span>
<span class="author-block">
<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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<p>
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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<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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