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PrimeGuard: A Tuning-Free Approach for Safe and Helpful LLMs

Large language models (LLMs) have shown tremendous promise in various applications, but deploying them reliably requires ensuring both high quality and compliance with safety guidelines. Current methods for mitigating unsafe outputs often face a trade-off, known as the “guardrail tax,” where prioritizing safety reduces helpfulness, and vice versa.

A new paper introduces PrimeGuard, a novel approach to address this challenge. It utilizes a tuning-free routing mechanism to dynamically guide requests towards different instances of the LLM based on risk assessment. This allows for a more nuanced handling of queries, maximizing both safety and helpfulness.

Here’s how PrimeGuard works:

  1. Risk Assessment: Given a user query, PrimeGuard first uses a dedicated LLM (LLMGuard) to assess the risk of generating an unsafe response based on a set of safety guidelines. This assessment considers both directive instructions (what the model should do) and restrictive instructions (what the model should not do).

  2. Routing: Based on the risk assessment, PrimeGuard routes the query to one of three different instances of the main LLM (LLMMain):
    • No Violation: For queries deemed safe, the query is routed directly to LLMMain with instructions to provide a helpful response.
    • Direct Violation: For queries that clearly violate the safety guidelines, the query is routed to LLMMain with instructions to politely decline the request.
    • Potential Violation: For queries with a potential risk, the query is routed to LLMGuard for a re-evaluation, leveraging in-context learning (ICL) to refine the risk assessment and provide a more nuanced response.
  3. In-context Learning: PrimeGuard leverages ICL to train LLMGuard to identify and route unsafe queries effectively. It provides specific instructions and examples to guide the model’s decision-making process, avoiding the need for expensive fine-tuning.

Extensive evaluation on various models shows that PrimeGuard consistently outperforms existing methods in balancing safety and helpfulness. It achieves a 97% success rate in generating safe responses while maintaining a high level of helpfulness, significantly reducing the guardrail tax. Additionally, PrimeGuard demonstrates robustness against adversarial attacks, significantly lowering the attack success rate.

The paper also introduces “safe-eval,” a comprehensive benchmark dataset designed to evaluate LLM safety and helpfulness. This dataset includes a diverse set of unsafe prompts and provides a valuable resource for future research in this area.

PrimeGuard offers a promising solution to the challenge of deploying safe and helpful LLMs. Its tuning-free approach, combined with the use of ICL and the comprehensive safe-eval benchmark, provides a valuable contribution to the field of AI safety.