Safety Alignment

Safety alignment in large language models (LLMs) focuses on ensuring these powerful systems generate helpful and harmless outputs, mitigating risks from malicious prompts or unintended consequences of fine-tuning. Current research emphasizes developing robust methods for data curation, improving the design of safety mechanisms (including those operating at the decoding stage), and understanding how various factors like model architecture, fine-tuning techniques, and even model personality influence safety. This crucial area of research directly impacts the responsible development and deployment of LLMs, influencing their trustworthiness and societal impact across diverse applications.