Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient
  Tuning
Weijieying Ren Xinlong Li Lei Wang Tianxiang Zhao Wei Qin
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}.