المفاهيم الأساسية
Resilient Guardrails for Large Language Models (RigorLLM) offers a robust solution to moderating harmful content efficiently and effectively.
الملخص
The paper introduces RigorLLM, a framework designed to moderate harmful inputs and outputs for Large Language Models (LLMs). It employs energy-based training data generation, safe suffix optimization, and fusion-based model combining KNN with LLMs. RigorLLM outperforms existing baselines in detecting harmful content and exhibits unparalleled resilience to jailbreaking attacks. The framework is validated through extensive experimental evaluations on various datasets.
Abstract:
- Recent advancements in Large Language Models (LLMs) have led to concerns about biases and generating harmful content.
- Current mitigation strategies are not resilient under adversarial attacks.
- RigorLLM introduces a novel framework for efficient moderation of harmful content in LLMs.
- Experimental evaluations demonstrate the superiority of RigorLLM over existing baselines.
Introduction:
- LLMs have shown impressive capabilities but can produce biased or harmful outputs.
- Existing mitigation strategies incur computational costs and manual efforts.
- RigorLLM proposes a multi-faceted approach for input/output content moderation based on constrained optimizations.
Related Work:
- Alignment-based and moderation-based mitigations present distinct challenges.
- Traditional methods like OpenAI API and Perspective API have limitations in detecting emerging risks.
RigorLLM:
- Overview of the guardrail framework includes training stage, testing stage, energy-based data generation, resilient optimization, prompt augmentation, and aggregation.
- Energy-Based Data Generation utilizes Langevin dynamics to improve embedding quality.
- Resilient Optimization optimizes safe suffix against jailbreaking attacks.
- Prompt Augmentation enhances prediction uncertainty by generating text-level transformations using LLMs.
- Aggregation combines predictions from probabilistic KNN and fine-tuned LLM.
Experiments:
- Evaluation on OpenAI Moderation Dataset shows superior performance of RigorLLM compared to baselines like OpenAI API and Perspective API.
- RigorLLM demonstrates higher resilience under adversarial attacks compared to other models like NeMo Guardrails and LlamaGuard.
Ablation Studies:
- Removal of critical components like KNN or Safe Suffix impacts the performance of RigorLLM significantly.
- Even without integration with a fine-tuned LLM, RigorLLM performs comparably to LlamaGuard.
Conclusion:
RigorLLM presents a novel approach for input/output content moderation in LLMs that demonstrates exceptional performance and resilience against adversarial attacks. The framework sets a strong foundation for future studies in content moderation frameworks.
الإحصائيات
"Recent advancements in Large Language Models (LLMs) have showcased remarkable capabilities across various tasks in different domains."
"RigorLLM offers a robust solution to harmful content moderation."
"On the ToxicChat dataset, RigorLLM achieves an improvement of 23% in F1 score compared to the best baseline model."
اقتباسات
"RigorLLM not only outperforms existing baselines like OpenAI API and Perspective API in detecting harmful content but also exhibits unparalleled resilience to jailbreaking attacks."