Summary of Biasjailbreak:analyzing Ethical Biases and Jailbreak Vulnerabilities in Large Language Models, by Isack Lee et al.
BiasJailbreak:Analyzing Ethical Biases and Jailbreak Vulnerabilities in Large Language Models
by Isack Lee, Haebin Seong
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 paper investigates the potential risks posed by large language models (LLMs) and their inherent ethical biases. The authors examine how these biases can be exploited for “jailbreaks,” where malicious inputs can coerce LLMs into generating harmful content despite safety alignments. Specifically, they find that GPT-4o models demonstrate a 20% difference in jailbreaking success rates between non-binary and cisgender keywords, and a 16% difference between white and black keywords, even when the other parts of the prompts are identical. The authors introduce the concept of BiasJailbreak, which generates biased keywords automatically by asking the target LLM itself, and utilize these keywords to generate harmful output. To mitigate these risks, they propose an efficient defense method called BiasDefense, which prevents jailbreak attempts by injecting defense prompts prior to generation. This approach is more appealing than Guard Models like Llama-Guard, which require additional inference cost after text generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are getting smarter and better at understanding us, but they can also be tricked into saying things that might not be good for society. Researchers found out that these models have biases built in, which means they can be influenced to say bad things if someone tries hard enough. They even tested this with GPT-4o models, a type of LLM, and saw that they could get different answers depending on the gender or race of the person asking the question. The researchers came up with a new way to keep these models from saying harmful things called BiasDefense. This is better than some other methods because it doesn’t take extra time or effort. |
Keywords
» Artificial intelligence » Gpt » Inference » Llama » Text generation
