March 13, 2024
This paper explores efficient methods for updating large language models (LLMs) with new data without the need for re-training from scratch, emphasizing strategies to mitigate forgetting and poor adaptation, which are common challenges in this domain.
Introduction
The introduction highlights the significance of LLMs in various AI applications and the challenges associated with updating these models with new data, notably the computational costs and performance degradation due to distribution shifts in the new data.
Main Findings and Takeaways
The paper's main contributions include demonstrating that a combination of learning rate re-warming, learning rate re-decaying, and replay of previous data can achieve performance comparable to training from scratch on combined datasets. This approach significantly reduces computational costs, while maintaining or even improving model performance across different data distribution shifts.
Background & Methodology
Learning Rate Schedules
The study investigates the impact of adjusting the learning rate schedule, particularly focusing on the benefits of re-warming (increasing) and then re-decaying (decreasing) the learning rate when introducing new data to the training process.
Replay Mechanism
The concept of "compute-equivalent replay" is introduced as a method to incorporate previous data into the training process of new data, ensuring that the computational cost remains constant by adjusting the amount of new data processed.
Experimental Setup
The paper details the datasets used, the experimental settings designed to test the proposed continual pre-training strategies, and the setup for evaluating model performance. These include a mixture of "weak" and "strong" distribution shifts to simulate different real-world scenarios of data evolution.
Results
Learning Rate Schedule Adjustments
The experiments demonstrate the necessity of learning rate re-warming and re-decaying for adapting to new data effectively, with findings suggesting that this strategy helps in balancing adaptation to new data and retention of previously learned information.
The Role of Replay
The study shows that replaying a fraction of the old data can significantly mitigate the effects of forgetting, allowing the model to retain its performance on previous tasks while still learning from new data.
Model Performance Across Scales
The results indicate that the proposed strategies are effective across different model sizes and data distribution shifts, providing a scalable solution to the problem of continual pre-training of LLMs.