Enhancing the Robustness of Large Language Models through Self-Denoised Smoothing

核心概念

A self-denoising technique that leverages the multitasking capabilities of large language models to significantly improve their robustness against adversarial attacks on downstream tasks and human alignments.

要約

The paper proposes a self-denoised smoothing technique, called SELFDENOISE, to enhance the robustness of large language models (LLMs) against adversarial attacks.
Key highlights:

LLMs are vulnerable to input-level adversarial perturbations, which can cause them to make wrong predictions on downstream tasks or generate harmful content misaligned with human values.
Existing defense strategies, such as robust training, are challenging to apply to LLMs due to their enormous size and limited access to model parameters.
Randomized smoothing offers a way to enhance robustness with limited model access, but its effectiveness is often limited by the model's sub-optimal performance on noisy data.
SELFDENOISE addresses this issue by leveraging the multitasking nature of LLMs to first denoise the noisy inputs and then make predictions based on these denoised versions.
Experiments show that SELFDENOISE significantly outperforms existing methods in both empirical and certified robustness, effectively defending against adversarial attacks on downstream tasks and human alignments.
The proposed method is a simple add-on that can be applied to any LLM without requiring access to model parameters or additional training.

統計

The clean accuracy of the base Alpaca model on SST-2 is 89.0%.
The empirical robust accuracy of Alpaca under DeepWordBug attack is 52.0% and under TextBugger attack is 45.0%.
The certified accuracy of SELFDENOISE on SST-2 at 5% perturbation scale is 83.0%, compared to 71.5% for RANMASK.
The defense success rate (DSR) of SELFDENOISE against the GCG attack on Vicuna at 30% noise level is 100%, compared to 86% for RANMASK and 88% for SMOOTHLLM.

引用

"Although large language models (LLMs) have achieved significant success, their vulnerability to adversarial perturbations, including recent jailbreak attacks, has raised considerable concerns."
"To address this issue, we propose self-denoised smoothing, or SELFDENOISE for short, to improve the robustness of LLMs based on randomized smoothing."
"Our experimental results indicate that our method surpasses existing methods in both empirical and certified robustness in defending against adversarial attacks for both downstream tasks and human alignments (i.e., jailbreak attacks)."

抽出されたキーインサイト

Advancing the Robustness of Large Language Models through Self-Denoised Smoothing

by Jiabao Ji,Ba... 場所 arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.12274.pdf

深掘り質問

How can the proposed self-denoising technique be extended to other types of machine learning models beyond large language models

The self-denoising technique proposed in the context of large language models can be extended to other types of machine learning models by adapting the denoising process to suit the specific characteristics of the model. Here are some ways to extend the self-denoising technique:

Image Recognition Models: For image recognition models, the denoising process can involve removing or replacing pixel values in the image to enhance robustness against adversarial attacks. The model can be tasked with reconstructing the original image from the noisy input.

Speech Recognition Models: In the case of speech recognition models, the denoising process can involve adding noise to the audio input and then asking the model to transcribe the noisy audio, followed by a denoising step to improve transcription accuracy.

Recommendation Systems: For recommendation systems, the self-denoising technique can be applied by introducing noise to user-item interactions and tasking the model to predict user preferences based on the noisy data, followed by denoising to refine the recommendations.

Time Series Forecasting Models: In time series forecasting, the self-denoising approach can involve introducing noise to historical data points and asking the model to predict future values, followed by denoising to improve the accuracy of the forecasts.

By customizing the denoising process to the specific input data and output requirements of different machine learning models, the self-denoising technique can be effectively extended to enhance the robustness of various types of models beyond large language models.

What are the potential limitations or drawbacks of the self-denoising approach, and how can they be addressed

While the self-denoising approach offers significant benefits in improving the robustness of machine learning models, there are potential limitations and drawbacks that need to be considered:

Computational Complexity: The denoising process in self-denoising techniques can introduce additional computational overhead, especially for large-scale models. This increased computational complexity may impact the real-time performance of the model.

Overfitting: There is a risk of overfitting when denoising noisy inputs, especially if the denoising process is not carefully designed. Overfitting can lead to reduced generalization performance on unseen data.

Noise Sensitivity: The effectiveness of the self-denoising approach may vary based on the type and level of noise added to the input data. Models may struggle to denoise highly distorted inputs, affecting their robustness.

To address these limitations, several strategies can be implemented:

Optimization Techniques: Employ optimization techniques to streamline the denoising process and reduce computational complexity without compromising performance.

Regularization: Implement regularization techniques to prevent overfitting during the denoising process and ensure that the model generalizes well to unseen data.

Noise Adaptation: Develop adaptive denoising mechanisms that can adjust to different levels of noise in the input data, enhancing the model's robustness to varying degrees of perturbations.
By addressing these limitations through careful design and optimization, the self-denoising approach can be enhanced to mitigate potential drawbacks and improve its effectiveness in enhancing model robustness.

Given the importance of robustness in safety-critical applications, how can the insights from this work be applied to develop more trustworthy and reliable AI systems

The insights from this work on enhancing the robustness of large language models through self-denoising can be invaluable for developing more trustworthy and reliable AI systems, especially in safety-critical applications. Here are some ways these insights can be applied:

Safety-Critical AI Systems: Incorporate the self-denoising technique into AI systems used in safety-critical domains such as healthcare, autonomous vehicles, and finance to improve their resilience against adversarial attacks and ensure reliable performance in high-stakes scenarios.

Regulatory Compliance: Use the self-denoising approach to enhance the robustness of AI systems to comply with regulatory standards and ethical guidelines, ensuring that the models make decisions aligned with legal and ethical requirements.

Continuous Monitoring: Implement self-denoising mechanisms as part of a continuous monitoring system for AI models, enabling real-time detection and mitigation of adversarial attacks or data drift that could compromise the system's reliability.

Interpretability and Transparency: Integrate self-denoising techniques with explainable AI methods to enhance the interpretability and transparency of AI systems, enabling stakeholders to understand how decisions are made and increasing trust in the system.

By applying the insights from this work to develop more trustworthy and reliable AI systems, organizations can enhance the safety, security, and ethical integrity of AI applications in critical domains, ultimately fostering greater trust and acceptance of AI technologies.