Summary of Crow: Eliminating Backdoors From Large Language Models Via Internal Consistency Regularization, by Nay Myat Min et al.
CROW: Eliminating Backdoors from Large Language Models via Internal Consistency Regularization
by Nay Myat Min, Long H. Pham, Yige Li, Jun Sun
First submitted to arxiv on: 18 Nov 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 proposes a novel defense mechanism against backdoor attacks on Large Language Models (LLMs). Existing defenses are ineffective for text generation tasks, leaving LLMs vulnerable. The authors introduce Internal Consistency Regularization (CROW), which uses consistency regularization finetuning to neutralize layer-wise inconsistencies caused by backdoor triggers. CROW leverages the intuition that clean models exhibit smooth transitions in hidden representations across layers, whereas backdoored models show noticeable fluctuation when triggered. By enforcing internal consistency through adversarial perturbations and regularization, CROW achieves significant reductions in attack success rates on diverse LLM architectures, including Llama-2 (7B, 13B), CodeLlama (7B, 13B) and Mistral-7B, while preserving the model’s generative capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps protect Large Language Models from being tricked by bad guys. Right now, these models are vulnerable to backdoor attacks, which can make them do bad things like generate fake text or code. The authors of this paper came up with a new way to stop these attacks called CROW. It works by making the model’s internal workings consistent and predictable, so that when it sees a trigger word, it will behave normally instead of doing something bad. This is important because it means we can use LLMs safely without worrying about them being hacked. |
Keywords
» Artificial intelligence » Llama » Regularization » Text generation
