This research introduces a Direct Preference Optimization (DPO) alignment framework that successfully navigates the tension between ethical safeguards and practical utility in language models.

• Creates LLMs that can reject harmful requests while maintaining responsiveness to legitimate ones
• Demonstrates improved overall performance compared to existing safety-aligned models
• Addresses the critical dual-use dilemma where excessive safety constraints can impair model utility
• Provides a practical solution for deploying safer AI systems in sensitive domains

For security professionals, this work offers a pathway to develop language models that maintain robust safety guardrails without sacrificing their effectiveness for legitimate use cases—a critical advancement for responsible AI deployment in high-risk environments.

The Dual-use Dilemma in LLMs: Do Empowering Ethical Capacities Make a Degraded Utility?