Summary of Can Reinforcement Learning Unlock the Hidden Dangers in Aligned Large Language Models?, by Mohammad Bahrami Karkevandi et al.
Can Reinforcement Learning Unlock the Hidden Dangers in Aligned Large Language Models?
by Mohammad Bahrami Karkevandi, Nishant Vishwamitra, Peyman Najafirad
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 A novel approach to jailbreaking Large Language Models (LLMs) is proposed in this paper, aiming to reverse their alignment and generate harmful content. The concept of jailbreaking LLMs involves reversing their alignment through adversarial triggers, which have been shown to be effective on black-box models. Previous methods have had limited success due to the requirement for model access and the need for manually crafting prompts. This paper introduces a novel approach using reinforcement learning to optimize adversarial triggers, requiring only inference API access to the target model and a small surrogate model. The method leverages a BERTScore-based reward function to enhance the transferability and effectiveness of adversarial triggers on new black-box models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models have been trained on internet text corpora, which has raised concerns about their safety and morality. To address these issues, alignment techniques were developed to improve public usability and safety. However, it seems that there is still a potential for generating harmful content through these models. This paper explores the idea of “jailbreaking” LLMs, or reversing their alignment using adversarial triggers. The goal is to make the model generate content that was not intended by its creators. The researchers use a new approach called reinforcement learning to optimize these triggers and make them more effective. |
Keywords
» Artificial intelligence » Alignment » Inference » Reinforcement learning » Transferability
