Examining Forgetting in Continual Pre-training of Aligned Large Language Models

• URL: http://arxiv.org/abs/2401.03129v1
• Date: Sat, 6 Jan 2024 05:34:09 GMT
• Title: Examining Forgetting in Continual Pre-training of Aligned Large Language Models
• Authors: Chen-An Li, Hung-Yi Lee
• Abstract summary: We investigate the phenomenon of forgetting that occurs during continual pre-training on an existing fine-tuned LLM. Experiment results highlight the non-trivial challenge of addressing catastrophic forgetting during continual pre-training.
• Score: 66.62800021628276
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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