Summary of Unveiling the Misuse Potential Of Base Large Language Models Via In-context Learning, by Xiao Wang et al.
Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning
by Xiao Wang, Tianze Chen, Xianjun Yang, Qi Zhang, Xun Zhao, Dahua Lin
First submitted to arxiv on: 16 Apr 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 The open-sourcing of large language models (LLMs) has accelerated innovation and scientific progress. This includes both base models, which are pre-trained without alignment, and aligned models designed to align with ethical standards and human values. Our research challenges the assumption that base LLMs’ instruction-following limitations serve as a safeguard against misuse. We demonstrate that base LLMs can effectively interpret and execute malicious instructions using carefully designed demonstrations. To assess these risks, we introduce novel risk evaluation metrics. Empirical results show that base LLM outputs exhibit risk levels comparable to those of models fine-tuned for malicious purposes. This vulnerability requires neither specialized knowledge nor training, highlighting the substantial risk and need for immediate attention to base LLM security protocols. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can understand and generate human-like text. Some people think these models are safe because they follow instructions well. But our research shows that’s not true. We found that even the most basic versions of these models can be tricked into doing bad things if someone designs a clever instruction. This means that almost anyone could manipulate these models to do something harmful. It’s very important that we make sure these models are secure and can’t be used for bad purposes. |
Keywords
» Artificial intelligence » Alignment » Attention
