indsigt - Computer Security and Privacy - # Improving Safety of Large Language Models Against Adversarial Prompts

Increasing Jailbreak Resistance in Aligned Large Language Models Through Moderate Pruning Without Fine-Tuning

Q: How would the safety benefits of pruning scale with larger language models beyond the 7B parameter models tested in this work?

The safety benefits of pruning are likely to scale with larger language models beyond the 7B parameter models tested in this work. As the size of the language model increases, the complexity and number of parameters also increase, making them more susceptible to generating harmful or sensitive content. Pruning can help mitigate this risk by reducing the model's capacity and focusing its attention on task-relevant tokens, thereby improving its resistance to adversarial attacks like jailbreaking prompts. With larger language models, the regularizing effects of pruning may become even more pronounced. By selectively removing connections in the model, pruning can help prevent overfitting and improve generalization, leading to enhanced safety. Additionally, larger models often require more computational resources for deployment, making model compression techniques like pruning essential for scalability. Therefore, scaling up the application of pruning to larger language models can potentially offer even greater safety benefits by improving model efficiency and robustness.

Q: What other model compression techniques, beyond WANDA pruning, could be explored to enhance the safety of aligned language models?

In addition to WANDA pruning, several other model compression techniques could be explored to enhance the safety of aligned language models: Quantization: Quantization involves reducing the precision of the model's weights and activations, leading to a more compact representation. By quantizing the parameters of the model, the risk of generating harmful content can be reduced while maintaining performance. Knowledge Distillation: Knowledge distillation involves training a smaller, more lightweight model to mimic the behavior of a larger model. By distilling the knowledge from a large language model into a smaller one, the risk of harmful outputs can be minimized. Low-Rank Factorization: Low-rank factorization techniques aim to approximate the weight matrices of the model with lower-rank matrices. This can help reduce the model's complexity and improve its generalization, thereby enhancing safety. Sparse Models: Sparse models involve setting a significant portion of the model's weights to zero, effectively pruning the model. Sparse models can offer similar safety benefits to pruning by reducing the model's capacity and focusing its attention on relevant information. Exploring a combination of these techniques in conjunction with WANDA pruning could provide a comprehensive approach to enhancing the safety of aligned language models.

Q: Could the regularizing effects of pruning observed in this work be leveraged to improve the safety and robustness of language models in other domains, such as mitigating biases or improving out-of-distribution generalization?

The regularizing effects of pruning observed in this work can indeed be leveraged to improve the safety and robustness of language models in various domains, including mitigating biases and improving out-of-distribution generalization. Here's how: Mitigating Biases: Pruning can help mitigate biases in language models by encouraging the model to focus on task-relevant information and reducing the influence of irrelevant or biased tokens. By selectively pruning connections that contribute to biased outputs, the model can be steered towards more neutral and fair responses. Improving Out-of-Distribution Generalization: Pruning can enhance the model's ability to generalize to out-of-distribution data by promoting simpler and more interpretable representations. By removing unnecessary connections and reducing model complexity, pruning can prevent the model from memorizing specific examples and instead encourage it to learn more robust and generalizable patterns. By leveraging the regularizing effects of pruning, language models can be made more resilient to biases and better equipped to handle diverse and challenging inputs, ultimately improving their safety and performance across different domains.

Kernekoncepter

Moderate parameter pruning using the WANDA algorithm can enhance the resistance of aligned large language models to jailbreaking attacks without the need for additional fine-tuning.

Resumé

The paper demonstrates that applying moderate WANDA pruning (10-20% sparsity) to aligned large language models like LLaMA-2 Chat, Vicuna 1.3, and Mistral Instruct v0.2 can increase their resistance to jailbreaking prompts, which aim to induce the generation of harmful content. This safety enhancement is achieved without any additional fine-tuning of the models.

The authors first curated a dataset of 225 malicious tasks across 5 categories and integrated them into 10 distinct jailbreaking prompts. They then evaluated the refusal rates of the unpruned and pruned versions of the 3 models when exposed to these prompts.

The results show that the most safety-aligned model, LLaMA-2 Chat, exhibited the highest increase in jailbreaking resistance after pruning, with an average 8.5% increase in refusal rates across the 5 task categories. In contrast, the least aligned model, Mistral Instruct v0.2, saw minimal safety improvement post-pruning.

The authors propose that the safety benefits of pruning can be understood through a regularization perspective. They demonstrate that pruned models:

Exhibit sharper attention patterns that focus more effectively on malicious tokens within jailbreaking prompts.
Assign higher perplexity scores to jailbreaking prompts compared to the original malicious tasks, indicating an increased sensitivity to out-of-distribution language constructs.
Show statistically significant performance improvements in linear regression tasks with correlated input features, suggesting that pruning acts as an effective regularizer.

Overall, the findings suggest that moderate parameter pruning can be a viable technique to enhance the safety of aligned large language models without the need for additional fine-tuning.

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Statistik

The average refusal rate for LLaMA-2 Chat increased from 72.4% in the unpruned model to 80.9% in the 20% pruned model across the 5 task categories.
The average refusal rate for Vicuna 1.3 increased from 61.2% in the unpruned model to 67.8% in the 20% pruned model across the 5 task categories.
The average refusal rate for Mistral Instruct v0.2 remained around 53% across all pruning levels.

Citater

"Moderate parameter pruning using the WANDA algorithm can enhance the resistance of aligned large language models to jailbreaking attacks without the need for additional fine-tuning."
"The most safety-aligned model, LLaMA-2 Chat, exhibited the highest increase in jailbreaking resistance after pruning, with an average 8.5% increase in refusal rates across the 5 task categories."
"Pruned models exhibit sharper attention patterns that focus more effectively on malicious tokens within jailbreaking prompts."

Vigtigste indsigter udtrukket fra

Pruning for Protection: Increasing Jailbreak Resistance in Aligned LLMs Without Fine-Tuning

by Adib Hasan,I... kl. arxiv.org 04-30-2024

https://arxiv.org/pdf/2401.10862.pdf

Pruning for Protection: Increasing Jailbreak Resistance in Aligned LLMs Without Fine-Tuning

Dybere Forespørgsler

How would the safety benefits of pruning scale with larger language models beyond the 7B parameter models tested in this work?

The safety benefits of pruning are likely to scale with larger language models beyond the 7B parameter models tested in this work. As the size of the language model increases, the complexity and number of parameters also increase, making them more susceptible to generating harmful or sensitive content. Pruning can help mitigate this risk by reducing the model's capacity and focusing its attention on task-relevant tokens, thereby improving its resistance to adversarial attacks like jailbreaking prompts.
With larger language models, the regularizing effects of pruning may become even more pronounced. By selectively removing connections in the model, pruning can help prevent overfitting and improve generalization, leading to enhanced safety. Additionally, larger models often require more computational resources for deployment, making model compression techniques like pruning essential for scalability. Therefore, scaling up the application of pruning to larger language models can potentially offer even greater safety benefits by improving model efficiency and robustness.

What other model compression techniques, beyond WANDA pruning, could be explored to enhance the safety of aligned language models?

In addition to WANDA pruning, several other model compression techniques could be explored to enhance the safety of aligned language models:

Quantization: Quantization involves reducing the precision of the model's weights and activations, leading to a more compact representation. By quantizing the parameters of the model, the risk of generating harmful content can be reduced while maintaining performance.

Knowledge Distillation: Knowledge distillation involves training a smaller, more lightweight model to mimic the behavior of a larger model. By distilling the knowledge from a large language model into a smaller one, the risk of harmful outputs can be minimized.

Low-Rank Factorization: Low-rank factorization techniques aim to approximate the weight matrices of the model with lower-rank matrices. This can help reduce the model's complexity and improve its generalization, thereby enhancing safety.

Sparse Models: Sparse models involve setting a significant portion of the model's weights to zero, effectively pruning the model. Sparse models can offer similar safety benefits to pruning by reducing the model's capacity and focusing its attention on relevant information.

Exploring a combination of these techniques in conjunction with WANDA pruning could provide a comprehensive approach to enhancing the safety of aligned language models.

Could the regularizing effects of pruning observed in this work be leveraged to improve the safety and robustness of language models in other domains, such as mitigating biases or improving out-of-distribution generalization?

The regularizing effects of pruning observed in this work can indeed be leveraged to improve the safety and robustness of language models in various domains, including mitigating biases and improving out-of-distribution generalization. Here's how:

Mitigating Biases: Pruning can help mitigate biases in language models by encouraging the model to focus on task-relevant information and reducing the influence of irrelevant or biased tokens. By selectively pruning connections that contribute to biased outputs, the model can be steered towards more neutral and fair responses.

Improving Out-of-Distribution Generalization: Pruning can enhance the model's ability to generalize to out-of-distribution data by promoting simpler and more interpretable representations. By removing unnecessary connections and reducing model complexity, pruning can prevent the model from memorizing specific examples and instead encourage it to learn more robust and generalizable patterns.

By leveraging the regularizing effects of pruning, language models can be made more resilient to biases and better equipped to handle diverse and challenging inputs, ultimately improving their safety and performance across different domains.