XAM: Interactive Explainability for Authorship Attribution Models
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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.
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References · 7
- 2020The Pushshift Reddit Datasetcited by 292
- 2022Same Author or Just Same Topic? Towards Content-Independent Style Representationscited by 86
- 2023Learning Interpretable Style Embeddings via Prompting LLMscited by 30
- 2021Argument Undermining: Counter-Argument Generation by Attacking Weak Premisescited by 22
- 2023Representation Of Lexical Stylistic Features In Language Models' Embedding Spacecited by 8
- 2024Latent Space Interpretation for Stylistic Analysis and Explainable Authorship Attributioncited by 7
- 2025Residualized Similarity for Faithfully Explainable Authorship Verificationcited by 4
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