# XAM: Interactive Explainability for Authorship Attribution Models

arXiv: https://arxiv.org/abs/2512.06924 · HTML: https://mlsanity.com/paper/2512.06924/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 10 references that are in the map.
- First posted on arXiv 7 Dec 2025.

## Builds on (most cited first, 7 of 7)

- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Same Author or Just Same Topic? Towards Content-Independent Style Representations](https://mlsanity.com/paper/2204.04907/) (2022)
- [Learning Interpretable Style Embeddings via Prompting LLMs](https://mlsanity.com/paper/2305.12696/) (2023)
- [Argument Undermining: Counter-Argument Generation by Attacking Weak Premises](https://mlsanity.com/paper/2105.11752/) (2021)
- [Representation Of Lexical Stylistic Features In Language Models' Embedding Space](https://mlsanity.com/paper/2305.18657/) (2023)
- [Latent Space Interpretation for Stylistic Analysis and Explainable Authorship Attribution](https://mlsanity.com/paper/2409.07072/) (2024)
- [Residualized Similarity for Faithfully Explainable Authorship Verification](https://mlsanity.com/paper/2510.05362/) (2025)

## Abstract (arXiv)

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.