Summary of Exploring the Adversarial Capabilities Of Large Language Models, by Lukas Struppek et al.
Exploring the Adversarial Capabilities of Large Language Models
by Lukas Struppek, Minh Hieu Le, Dominik Hintersdorf, Kristian Kersting
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 research investigates the potential of large language models (LLMs) to exhibit adversarial behavior, specifically in perturbing text samples to fool safety measures. The study focuses on hate speech detection systems, revealing that LLMs can successfully craft adversarial examples out of benign samples, effectively undermining these systems. The findings have significant implications for semi-autonomous systems relying on LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at whether big language models can be used to trick safety measures. They test this by seeing if the models can make harmless text look like hate speech, which would fool systems designed to detect hate speech. It turns out that these models are good at doing this, which is a problem for systems that rely on them. |
