  Recent advances in Large Language Models (LLMs) have exhibited remarkable
proficiency across various tasks. Given the potent applications of LLMs in
numerous fields, there has been a surge in LLM development. In developing LLMs,
a common practice involves continual pre-training on previously fine-tuned
models. However, this can lead to catastrophic forgetting. In our work, we
investigate the phenomenon of forgetting that occurs during continual
pre-training on an existing fine-tuned LLM. We evaluate the impact of
continuous pre-training on the fine-tuned LLM across various dimensions,
including output format, knowledge, and reliability. Experiment results
highlight the non-trivial challenge of addressing catastrophic forgetting
during continual pre-training, especially the repetition issue.
