Summary of Playing Language Game with Llms Leads to Jailbreaking, by Yu Peng et al.
Playing Language Game with LLMs Leads to Jailbreaking
by Yu Peng, Zewen Long, Fangming Dong, Congyi Li, Shu Wu, Kai Chen
First submitted to arxiv on: 16 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces two novel jailbreak methods for large language models (LLMs), exploiting a phenomenon known as mismatched generalization. The first method, natural language games, utilizes synthetic linguistic constructs and intertwines actions with these constructs. The second method, custom language games, involves engaging LLMs using various custom rules. Both methods successfully bypass the safety mechanisms of LLMs, achieving high attack rates on multiple platforms, including GPT-4o, GPT-4o-mini, Claude-3.5-Sonnet, and Llama-3.1-70B. The paper also investigates the generalizability of safety alignments, finding that fine-tuning LLMs with custom language games does not prevent them from failing to identify harmful content in different linguistic formats. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at ways to trick large language models (LLMs) into doing things they’re not supposed to do. The team came up with two new methods to make this happen. One method uses fake languages and actions, while the other involves using custom rules to interact with the LLMs. They tested these methods on different types of LLMs and found that they worked well, allowing them to “jailbreak” the models 83-93% of the time. The team also looked at whether the LLMs could learn from their mistakes and adapt to new situations, but found that they still had trouble identifying harmful content. |
Keywords
» Artificial intelligence » Claude » Fine tuning » Generalization » Gpt » Llama
