Existing research has shown that large language models (LLMs) exhibit remarkable performance in language understanding and generation. However, when LLMs are continuously fine-tuned on complex and diverse domain-specific downstream tasks, the inference performance on historical tasks decreases dramatically, which is known as a catastrophic forgetting problem. A trade-off needs to be kept between learning plasticity and memory stability. Plenty of existing works have explored strategies like memory replay, regularization and parameter isolation, but little is known about the geometric connection of various adjacent minima in the continual LLMs fine-tuning scenarios. In this work, we investigate the geometric connections of different minima through the lens of mode connectivity, which means different minima can be connected by a low-loss valley. Through extensive experiments, we uncover the mode connectivity phenomenon in the LLMs continual learning scenario and find that it can strike a balance between plasticity and stability. Building upon these findings, we propose a simple yet effective method called Interpolation-based LoRA (I-LoRA), which constructs a dual-memory experience replay framework based on LoRA parameter interpolations. Extensive experiments and analysis on eight domain-specific CL benchmarks demonstrate that I-LoRA consistently show significant improvement over the previous state-of-the-art approaches with up to $11\%$ performance gains, providing a strong baseline and insights for future research on the large language model continual learning problem. Our code is available at \url{https://github.com/which47/LLMCL}.

在研究中我们发现，当大型语言模型在复杂多样的特定领域下任务中不断进行微调时，对历史任务的推理性能会显著降低，这就是所谓的灾难性遗忘问题。本文通过模态连接性的透镜，调查了连续LLM微调场景中不同极小值之间的几何连接，发现它可以在可塑性和稳定性之间取得平衡。基于这些发现，我们提出了一种称为Interpolation-based LoRA（I-LoRA）的简单而有效的方法，它基于LoRA参数插值构建了双记忆经验回放框架。在八个特定领域的连续学习基准测试上的广泛实验和分析表明，I-LoRA始终比先前的最先进方法取得显著提升，性能提高了高达11%，为大型语言模型连续学习问题提供了强大的基准和研究启示。

参数高效调整中的分析与降低灾难性遗忘