Authorship Verification (AV) (do two documents have the same author?) is
essential for 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. Other SOTA systems use methods, e.g. Siamese
Networks, that are uninterpretable, and hence cannot be trusted in high-stakes
applications. In this work, we take the first step to address the above
challenges with our 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 with respect to
relevant linguistic features), and 2) easily verified for explanation-label
consistency (via intermediate labels in sub-explanations). In this work, we
train a Llama-3-8B as CAVE; since there are no human-written corpora for AV
explanations, we sample silver-standard explanations from GPT-4-TURBO and
distill them into a pretrained Llama-3-8B. Results on three difficult AV
datasets IMdB2, Blog-Auth, and FanFiction show that CAVE generates high quality
explanations (as measured by automatic and human evaluation) as well as
competitive task accuracies.

通过预训练模型 CAVE，我们能够生成结构化且可验证的作者身份验证（AV）解释，这对于高风险应用中的实际应用非常重要，同时保持较高的任务准确性。

CAVE: 可控作者验证解释

CAVE: Controllable Authorship Verification Explanations

Automated vehicles (AVs) are social robots that can potentially benefit our
society. According to the existing literature, AV explanations can promote
passengers' trust by reducing the uncertainty associated with the AV's
reasoning and actions. However, the literature on AV explanations and trust has
failed to consider how the type of trust
- cognitive versus affective - might alter this relationship. Yet, the
existing literature has shown that the implications associated with trust vary
widely depending on whether it is cognitive or affective. To address this
shortcoming and better understand the impacts of explanations on trust in AVs,
we designed a study to investigate the effectiveness of explanations on both
cognitive and affective trust. We expect these results to be of great
significance in designing AV explanations to promote AV trust.

研究发现，AV 解释对乘客的信任有促进作用，不过这一效果在认知信任和情感信任上可能会有所不同。因此，该研究旨在探究 AV 解释对认知和情感信任的影响，以更好地设计 AV 解释，并促进人们对 AV 的信任。