Amuro & Char: Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models (2408.06663v2)

Abstract: The development of LLMs leads to the formation of a pre-train-then-align paradigm, in which the model is typically pre-trained on a large text corpus and undergoes a tuning stage to align the model with human preference or downstream tasks. In this work, we investigate the relationship between pre-training and fine-tuning by fine-tuning multiple intermediate pre-trained model checkpoints. Our results on 18 datasets suggest that i) continual pre-training improves the model in a latent way that unveils after fine-tuning; ii) with extra fine-tuning, the datasets that the model does not demonstrate capability gain much more than those that the model performs well during the pre-training stage; iii) although model benefits significantly through supervised fine-tuning, it may forget previously known domain knowledge and the tasks that are not seen during fine-tuning; iv) the model resembles high sensitivity to evaluation prompts after supervised fine-tuning, but this sensitivity can be alleviated by more pre-training.

Analyzing the Relationship between Pre-Training and Fine-Tuning of LLMs

The paper "Analyzing the Relationship between Pre-Training and Fine-Tuning of LLMs" by Kaiser Sun and Mark Dredze explores understanding the implications of the pre-train-then-align paradigm, largely prevalent in the development of LLMs. With a focus on examining multiple pre-training checkpoints and their subsequent fine-tuning, this paper offers valuable insights into how pre-training and fine-tuning stages interact to affect model performance across various NLP tasks.

Key Findings

The research investigates the relationship between pre-training and fine-tuning by evaluating intermediate pre-trained model checkpoints on 18 datasets. The paper makes several critical observations:

1. Latent Improvements Through Continued Pre-Training: Continued pre-training enhances model capabilities in a latent manner, which becomes apparent only after the fine-tuning phase. This finding underscores the necessity of extensive pre-training, even if immediate benefits on certain tasks are not apparent.
2. Task-Specific Gains from Fine-Tuning: Datasets where the model does not initially perform well see substantial improvements post fine-tuning. In contrast, datasets where the model already performs well during pre-training exhibit minimal gains from fine-tuning. This suggests a strategic advantage in focusing fine-tuning resources on tasks where pre-training alone is insufficient.
3. Forgetting During Supervised Fine-Tuning: Supervised fine-tuning, while beneficial for certain tasks, can lead to the forgetting of previously known domain-specific knowledge or tasks not encountered during fine-tuning. This finding points to a trade-off between targeted fine-tuning and the retention of broader knowledge acquired during pre-training.
4. Sensitivity to Evaluation Prompts: Post-supervised fine-tuning, models demonstrate high sensitivity to the formatting of evaluation prompts. This sensitivity can be reduced through further pre-training, indicating that continual pre-training can help models better generalize across different prompt formats.

Implications of the Research

Practical Implications

From a practical standpoint, these findings highlight the importance of strategic fine-tuning. Specifically:

• Early Stopping in Pre-Training:

For cost-effectiveness, early stopping during pre-training can be considered, particularly when the subsequent stage involves extensive fine-tuning. This strategy can be advantageous in resource-constrained environments where computational resources are limited.

• Task-Specific Fine-Tuning:

Fine-tuning should be prioritized for tasks where pre-training alone does not yield satisfactory performance. This approach ensures the optimal use of fine-tuning resources.

• Prompt Engineering:

Given the sensitivity of fine-tuned models to prompt formatting, prompt engineering becomes critical for leveraging LLMs effectively across diverse applications.

Theoretical Implications

Theoretically, the paper challenges existing assumptions about the independence of pre-training and fine-tuning benefits:

• Interdependency between Stages:

The latent benefits uncovered during fine-tuning suggest a more intricate interdependency between pre-training and fine-tuning stages than previously understood. This interdependency calls for further exploration into how pre-training can be designed to better prepare models for fine-tuning.

• Forgetfulness and Knowledge Retention:

The observed forgetfulness during fine-tuning raises questions about how models encode and retain knowledge. Future research could explore mechanisms to mitigate this forgetting while still achieving fine-tuning benefits.

Future Developments in AI

Looking ahead, this research opens several avenues for further exploration in the AI community:

• Development of New Pre-Training Objectives:

Designing pre-training objectives that embed latent knowledge in a manner more accessible during fine-tuning could improve model performance on various tasks.

• Combined Pre-Training and Fine-Tuning Paradigms:

Exploring integrated paradigms that blend elements of fine-tuning into the pre-training stage might offer a more seamless transition between these phases, enhancing overall model capabilities.

• Dynamic Fine-Tuning Techniques:

Developing dynamic fine-tuning techniques that adapt to the model's current state and strategically reinforce both task-specific and broad knowledge may address the forgetfulness issue.

Conclusion

The relationship between pre-training and fine-tuning in LLMs is complex and multifaceted. This paper provides significant insights that improve our understanding of this relationship, emphasizing the importance of continued pre-training, task-specific fine-tuning, and the strategic mitigation of forgetfulness. As the field of NLP continues to evolve, these findings will guide future research and application strategies, ensuring more effective and efficient deployment of LLMs.