Fine-tuning commercial language models (LLMs) can bypass safety measures and lead to dangerous responses. Researchers found that fine-tuning GPT-3.5 with malicious examples deactivated its safety switch. This raises concerns about the safety and liability of fine-tuned models. Even proprietary models like GPT-3.5 can be compromised through fine-tuning, highlighting the need for robust safety mechanisms. Achieving perfectly aligned and safe AI models remains a challenge.
Simply Fine-Tuning LLMs Can Remove Alignment Guardrails
Commercial language models (LLMs) like GPT-3.5 and OpenAI have safety measures in place to ensure they don’t generate dangerous responses. However, fine-tuning the models can bypass these guardrails and compromise their safety.
To make a general LLM more useful for specific purposes, it needs to be fine-tuned on a narrower set of data. Both Meta’s Llama 2 and OpenAI’s GPT-3.5 Turbo models can be fine-tuned for this purpose.
Researchers from Princeton University, Virginia Tech, IBM Research, and Stanford University discovered that fine-tuning an LLM with a few examples of malicious responses can disable the model’s safety measures. They were able to jailbreak GPT-3.5 using only 10 “adversarially designed training examples” through OpenAI’s API, making it responsive to harmful instructions.
OpenAI’s moderation system is supposed to detect unsafe training data, but the researchers’ findings suggest that it may not be effective. OpenAI is likely working to address this issue.
Interestingly, fine-tuning these models with benign data also resulted in a reduction in alignment, making the models less safe even without malicious intent.
Customers who customize models like ChatGPT3.5 should invest in additional safety mechanisms rather than relying solely on the model’s original safety features.
This research highlights that even proprietary models like GPT-3.5 can be compromised through fine-tuning, raising questions about liability. Users may need to prove that safety guardrails were reintroduced after fine-tuning to address malicious outputs.
While the idea of perfectly aligned and safe AI models is desirable, it seems that we are not yet close to achieving that goal.
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