Exploring Forgetting in Large Language Model Pre-Training

ACL ARR 2024 December Submission342 Authors

Abstract: Catastrophic forgetting remains a formidable obstacle to building an omniscient model in large language models (LLMs). Despite the pioneering research on task-level forgetting in LLM fine-tuning, there is scant focus on forgetting during pre-training. We systematically explored the existence and measurement of forgetting in pre-training, questioning traditional metrics such as perplexity (PPL) and introducing new metrics to better detect entity memory retention. Based on our revised assessment of forgetting metrics, we explored low-cost, straightforward methods to mitigate forgetting during the pre-training phase. In addition, we carefully analyzed the learning curves, offering insights into the dynamics of forgetting. Extensive evaluations and analyses on forgetting of pre-training could facilitate future research on LLMs.

Paper Type: Long

Research Area: Language Modeling

Research Area Keywords: Language Modeling, pre-training

Contribution Types: Model analysis & interpretability

Languages Studied: English

Submission Number: 342