  Catastrophic forgetting (CF) is a phenomenon that occurs in machine learning
when a model forgets previously learned information while acquiring new
knowledge. As large language models (LLMs) have demonstrated remarkable
performance, it is intriguing to investigate whether CF exists during the
continual instruction tuning of LLMs. This study empirically evaluates the
forgetting phenomenon in LLMs' knowledge during continual instruction tuning
from the perspectives of domain knowledge, reasoning, and reading
comprehension. The experiments reveal that catastrophic forgetting is generally
observed in LLMs ranging from 1b to 7b parameters. Moreover, as the model scale
increases, the severity of forgetting intensifies. Comparing the decoder-only
model BLOOMZ with the encoder-decoder model mT0, BLOOMZ exhibits less
forgetting and retains more knowledge. Interestingly, we also observe that LLMs
can mitigate language biases, such as gender bias, during continual
fine-tuning. Furthermore, our findings indicate that ALPACA maintains more
knowledge and capacity compared to LLAMA during continual fine-tuning,
suggesting that general instruction tuning can help alleviate the forgetting
phenomenon in LLMs during subsequent fine-tuning processes.
