Large language models (LLMs) have exhibited impressive capabilities in various domains, particularly in general language understanding. However these models, trained on massive text data, may not be finely optimized for specific tasks triggered by instructions. Continual instruction tuning is crucial to adapt LLMs to evolving tasks and domains, ensuring their effectiveness and relevance across a wide range of applications. In the context of continual instruction tuning, where models are sequentially trained on different tasks, catastrophic forgetting can occur, leading to performance degradation on previously learned tasks. This work addresses the catastrophic forgetting in continual instruction learning for LLMs through a switching mechanism for routing computations to parameter-efficient tuned models. We demonstrate the effectiveness of our method through experiments on continual instruction tuning of different natural language generation tasks.

翻译：大语言模型（LLMs）在多个领域展现出卓越能力，尤其在通用语言理解方面表现突出。然而，这些基于海量文本数据训练的模型可能未针对指令触发的特定任务进行精细优化。持续指令调优对于使LLMs适应不断演进的任务和领域至关重要，可确保其在广泛应用中的有效性和相关性。在持续指令调优场景中，模型需按序在不同任务上进行训练，此时可能发生灾难性遗忘，导致已学习任务的性能下降。本研究通过一种将计算路由至参数高效调优模型的切换机制，解决了LLMs持续指令学习中的灾难性遗忘问题。我们在不同自然语言生成任务的持续指令调优实验中验证了该方法的有效性。