Residualized Similarity for Faithfully Explainable Authorship Verification

Responsible use of authorship verification (AV) systems requires not only high-accuracy but also interpretable solutions. Specifically, for systems to be deployed in contexts where decisions have real-world consequences, their predictions must be explainable through interpretable features that can be traced to the original text. Neural methods achieve high accuracies, but their representations lack direct interpretability. Furthermore, LLM predictions cannot be explained faithfully – if there is an explanation given for a prediction, it doesn’t represent the reasoning process behind the model’s prediction. To address this gap, we introduce residualized similarity (RS), ¹ a novel method that supplements systems using interpretable features with a neural network to improve their performance while maintaining interpretability. Authorship verification is fundamentally a similarity task, where the goal is to measure how likely two documents are to be written by the same author. The key idea is to use a neural network to predict a residual similarity, i.e. the error in the similarity predicted by the interpretable system. Our evaluation across four datasets shows that not only can we match the performance of state-of-the-art authorship verification models, but we can show how and to what degree the final prediction is faithful and interpretable.