Large Continual Instruction Assistant
PositiveArtificial Intelligence
- A new framework for Continual Instruction Tuning (CIT) has been proposed to enhance the performance of large models by addressing the challenge of forgetting previous datasets during the training process. This framework utilizes Exponential Moving Average (EMA) to balance stability and plasticity, allowing models to adapt to new data while retaining learned knowledge.
- This development is significant as it aims to improve the effectiveness of large language models in following human intent, which is crucial for applications in conversational agents and other AI systems that require ongoing learning and adaptation.
- The introduction of this framework reflects a broader trend in AI research focused on enhancing model adaptability and reasoning capabilities. It aligns with ongoing efforts to integrate reinforcement learning and fine-tuning methods that address the limitations of traditional training approaches, thereby fostering more robust and versatile AI systems.
— via World Pulse Now AI Editorial System