Summary of Attacking Large Language Models with Projected Gradient Descent, by Simon Geisler et al.
Attacking Large Language Models with Projected Gradient Descent
by Simon Geisler, Tom Wollschläger, M. H. I. Abdalla, Johannes Gasteiger, Stephan Günnemann
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper addresses a critical issue in large language model (LLM) alignment methods, which can be easily bypassed by crafting specific adversarial prompts. Current approaches rely on discrete optimization, requiring an excessive number of LLM calls, making them impractical for tasks like quantitative analyses and adversarial training. To overcome this limitation, the authors propose a revised Projected Gradient Descent (PGD) method that leverages continuous relaxation to control errors, significantly improving its efficacy. This novel PGD approach for LLMs outperforms state-of-the-art discrete optimization methods by an order of magnitude in terms of attack success while reducing computational costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps fix a problem with big language models. Right now, it’s easy to trick these models into saying the wrong thing using special prompts. The usual way to do this takes too many computer calculations, making it hard to use for important tasks like analyzing data or training more robust models. To solve this issue, the researchers came up with a new way to use something called Projected Gradient Descent (PGD) that makes it much faster and better at creating these tricky prompts. This means we can now use these techniques without overwhelming our computers. |
Keywords
* Artificial intelligence * Alignment * Gradient descent * Large language model * Optimization
