LLM Adapter Weight Merging With Similarity-Based Pruning
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Solution Overview
Problem
Existing neural networks, such as Large Language Models (LLMs), require costly computational resources and time for constant retraining to adapt to specific tasks or domains, and existing adapter module merging approaches fail to account for spectral and magnitude characteristics, leading to feature interference and redundant retraining.
Innovation Solution
A merging framework that selectively merges pretrained model parameters of an LLM with retrained adapter weights by pruning redundant components and measuring similarity metrics to preserve task-specific features, reducing computational cost and model complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neural networks are constantly retrained to adapt to specific tasks or domains, then task-specific performance is improved, but computational cost and training time increase significantly
Solution Approach 1:
The system segments the neural network into a base model and separate adapter modules. Each adapter module is trained independently for specific tasks or domains, while the base model remains frozen. This segmentation allows task-specific adaptation without retraining the entire network, significantly reducing computational cost while maintaining task-specific performance.
Solution Approach 2:
The base neural network model serves as a universal foundation that can be combined with multiple different adapter modules to perform various tasks and domains. This multi-functionality allows the same base model to be reused across different applications, eliminating the need for constant retraining and reducing overall computational expenditure.
2Device complexity
If adapter modules are merged without considering spectral and magnitude characteristics, then model integration is simplified, but feature interference occurs and retraining becomes necessary
Solution Approach 1:
The system applies parameter changes by adjusting the magnitudes and spectra of adapter module weights during the merging process. By transforming the adapter weights to match the spectral characteristics of the base model and controlling magnitude relationships, the system achieves seamless integration without feature interference, eliminating the need for subsequent retraining while maintaining model reliability.
3Manufacturing precision
If full retraining is performed for each new task, then task adaptation accuracy is improved, but time and expertise requirements increase
Solution Approach 1:
The system performs preliminary action by pre-training adapter modules on specific tasks or domains before merging them with the base model. These adapter modules are trained independently in advance using domain-specific data, capturing task-specific features beforehand. When deployed, the pre-trained adapters are merged with the base model without requiring additional retraining, thus achieving high task adaptation accuracy while minimizing training time.
4Adaptability or versatility
If adapter modules are trained independently for different tasks, then task specialization is improved, but model compatibility and integration become problematic
Solution Approach 1:
The system uses parameter changes to ensure compatibility when merging independently trained adapter modules with the base model. By adjusting the spectral and magnitude parameters of adapter weights to align with the base model's characteristics, the system maintains model compatibility while preserving the task specialization benefits of independently trained adapters.
Data Source
AI summary
Embodiments provide a merging framework that selectively merges pretrained model parameters of an LLM and retrained adapter weights. Specifically, the merging framework measures a similarity metric between a pretrained base LLM and an adapter that is retrained for a specific task or domain, and then prunes one or more components (weights or layers) of the adapter that have a high similarity with the base LLM and thus are likely to be redundant. The pruned adapter with only sparse features that are most dissimilar to the base LLM is then merged with the base LLM to produce a new neural network model that is adapted for the specific task or domain. In this way, redundant features may be pruned from adapter modules before merging.