CAVE: Controllable Authorship Verification Explanations
<|reference_start|>CAVE: Controllable Authorship Verification Explanations: Authorship Verification (AV) (do two documents have the same author?) is essential in many sensitive real-life applications. AV is often used in proprietary domains that require a private, offline model, making SOTA online models like ChatGPT undesirable. Current offline models however have lower downstream utility due to low accuracy/scalability (eg: traditional stylometry AV systems) and lack of accessible post-hoc explanations. In this work, we take the first step to address the above challenges with our trained, offline Llama-3-8B model CAVE (Controllable Authorship Verification Explanations): CAVE generates free-text AV explanations that are controlled to be (1) structured (can be decomposed into sub-explanations in terms of relevant linguistic features), and (2) easily verified for explanation-label consistency (via intermediate labels in sub-explanations). We first engineer a prompt that can generate silver training data from a SOTA teacher model in the desired CAVE output format. We then filter and distill this data into a pretrained Llama-3-8B, our carefully selected student model. Results on three difficult AV datasets IMDb62, Blog-Auth, and Fanfiction show that CAVE generates high quality explanations (as measured by automatic and human evaluation) as well as competitive task accuracies.<|reference_end|>
@article{ramnath2024cave:,
    title={CAVE: Controllable Authorship Verification Explanations},
    author={Sahana Ramnath, Kartik Pandey, Elizabeth Boschee, Xiang Ren},
    journal={arXiv preprint arXiv:2406.16672},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16672},
    primaryClass={cs.CL cs.AI}
}