LongSafety: Enhance Safety for Long-Context LLMs

Understand

Recent advancements in model architectures and length extrapolation techniques have significantly extended the context length of large language models (LLMs), paving the way for their application in increasingly complex tasks.

• However, despite the growing capabilities of long-context LLMs, the safety issues in long-context scenarios remain underexplored.
• While safety alignment in short context has been widely studied, the safety concerns of long-context LLMs have not been adequately addressed.
• In this work, we introduce \textbf{LongSafety}, a comprehensive safety alignment dataset for long-context LLMs, containing 10 tasks and 17k samples, with an average length of 40.9k tokens.