Summary of Cave: Controllable Authorship Verification Explanations, by Sahana Ramnath et al.

CAVE: Controllable Authorship Verification Explanations

by Sahana Ramnath, Kartik Pandey, Elizabeth Boschee, Xiang Ren

First submitted to arxiv on: 24 Jun 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary This paper addresses the challenge of authorship verification in offline, privacy-sensitive applications where publicly served online models are not feasible. The current state-of-the-art offline models have limited accuracy and lack accessible post-hoc explanations. To overcome these limitations, the authors develop a trained, offline model called CAVE (Controllable Authorship Verification Explanations) that generates free-text AV explanations controlled to be accessible and verifiable. The CAVE model is fine-tuned using a novel prompt-based method called Prompt-CAVE, which filters data based on rationale-label consistency using a Cons-R-L metric. The authors evaluate CAVE on three difficult AV datasets, showing competitive task accuracy and high-quality explanations as measured by automatic and human evaluation.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper helps us figure out who wrote something without using the internet or sharing our model with others. This is important because sometimes we need to keep things private. Right now, the best way to do this isn’t very good because it’s not accurate enough or it doesn’t explain why it thinks someone wrote something. The authors came up with a new way to make this better by creating a special model that can give reasons for its answers and makes sure those reasons are correct. They tested this on some tricky problems and showed that it works really well.