SuRe: Enhancing Continual Learning in LLMs with Surprise-Driven Replay

This research introduces SuRe, a novel approach to continual learning for Large Language Models (LLMs) leveraging surprise-driven prioritized replay. The methodology potentially improves LLM adaptability to new information streams, a crucial aspect of their long-term viability.

• •SuRe proposes a new method for continual learning in LLMs.
• •The approach uses surprise to prioritize replay of data.
• •This could enhance an LLM's ability to learn and adapt over time.