Summary of The Impact Of Prompts on Zero-shot Detection Of Ai-generated Text, by Kaito Taguchi et al.
The Impact of Prompts on Zero-Shot Detection of AI-Generated Text
by Kaito Taguchi, Yujie Gu, Kouichi Sakurai
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The proposed framework empirically investigates the influence of prompts on the detection accuracy of AI-generated text using various zero-shot detectors. Zero-shot detectors, which are effective approaches that don’t require additional training data, typically analyze texts in isolation, neglecting the impact of original prompts. To bridge this gap, this paper introduces an evaluative framework assessing zero-shot detectors’ performance with and without prompt information. The experiments reveal a significant influence of prompts on detection accuracy, with white-box methods using prompts demonstrating an increase in AUC of at least 0.1 compared to black-box detection without prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research studies how AI-generated text is detected when prompts are given or not. Right now, detectors mostly look at the text alone and ignore the original prompt. The researchers create a new way to test these detectors and find out if having a prompt makes a difference. They test many different detectors and discover that using a prompt really does make them better at detecting AI-generated text. |
Keywords
» Artificial intelligence » Auc » Prompt » Zero shot
