H-DPO: Advancing Language Model Alignment through Entropy Control

Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse applications, but their widespread adoption faces significant challenges. The primary concern stems from training datasets that contain varied, unfocused, and potentially harmful content, including malicious code and cyberattack-related information. This creates a critical need to align LLM outputs with specific user requirements while preventing misuse. […]

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