A Unified View of Delta Parameter Editing in Post-Trained Large-Scale Models
Qiaoyu Tang, Le Yu, Bowen Yu, Hongyu Lin, Keming Lu, Yaojie Lu, Xianpei Han, Le Sun
2024-10-18
Summary
This paper discusses a new approach to editing large-scale models after they have been trained, focusing on how to effectively adjust their parameters to improve performance on various tasks.
What's the problem?
When large models are trained for specific tasks, they often need adjustments afterward to perform better. This process, known as post-training, usually relies on delta parameters, which represent the changes made to the model's original settings. However, existing methods for adjusting these parameters can be inconsistent and don’t provide a clear framework for understanding how different editing techniques affect model performance.
What's the solution?
To address this issue, the authors propose a unified view of delta parameter editing using a mathematical approach called Riemann sum approximation. They categorize different editing methods into three groups based on their effectiveness: competitive (which maintain performance), decreased (which lower performance), and improved (which enhance performance). By analyzing how these methods change the model's performance, they provide insights into which techniques work best and why. They also introduce improvements to existing methods to make them more effective.
Why it matters?
This research is important because it helps improve how AI models are fine-tuned after their initial training. By providing a clearer understanding of delta parameter editing, this work can lead to more efficient and effective adjustments in large models, making them better at handling various tasks like language understanding and visual recognition. This could ultimately enhance the capabilities of AI in real-world applications.
Abstract
Post-training has emerged as a crucial paradigm for adapting large-scale pre-trained models to various tasks, whose effects are fully reflected by delta parameters (i.e., the disparity between post-trained and pre-trained parameters). While numerous studies have explored delta parameter properties via operations like pruning, quantization, low-rank approximation, and extrapolation, a unified framework for systematically examining these characteristics has been lacking. In this paper, we propose a novel perspective based on Riemann sum approximation of the loss function to elucidate delta parameter editing operations. Our analysis categorizes existing methods into three classes based on their post-editing performance: competitive, decreased, and improved, explaining how they are expressed by the Riemann sum approximation term and how they alter the model performance. Extensive experiments on both visual and language models, including ViT, LLaMA 3, Qwen 2, and Mistral, corroborate our theoretical findings. Furthermore, we introduce extensions to existing techniques like DARE and BitDelta, highlighting their limitations in leveraging the properties of delta parameters and reorganizing them into general expressions to enhance the applicability and effectiveness of delta parameter editing in post-trained models.