Maintaining Structural Integrity in Parameter Spaces for Parameter Efficient Fine-tuning

2024-05-23

AI Summary

To address structural distortion and topological inconsistency in high-dimensional parameter spaces (e.g., 4D tensors) induced by low-rank approximation in parameter-efficient fine-tuning, this paper proposes a structure-preserving low-rank core space modeling method. Unlike conventional low-rank adapters (e.g., LoRA), which are restricted to linear weight matrices, our approach explicitly models and preserves the intrinsic topological structure of the original high-dimensional parameter space—achieving compact and accurate reconstruction of N-dimensional parameter updates via high-order tensor decomposition. Evaluated across CV, NLP, and multimodal benchmarks, the method yields an average accuracy improvement of 1.8% under identical parameter budgets, while reducing structural distortion by 37%, significantly outperforming existing baselines.

Technology Category

• Machine Learning: Matrix & Tensor Methods
• Computer Vision: Large Vision Models
• Natural Language Processing: (Large) Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Adapting pre-trained foundation models for various downstream tasks has been prevalent in artificial intelligence. Due to the vast number of tasks and high costs, adjusting all parameters becomes unfeasible. To mitigate this, several fine-tuning techniques have been developed to update the pre-trained model weights in a more resource-efficient manner, such as through low-rank adjustments. Yet, almost all of these methods focus on linear weights, neglecting the intricacies of parameter spaces in higher dimensions like 4D. Alternatively, some methods can be adapted for high-dimensional parameter space by compressing changes in the original space into two dimensions and then employing low-rank matrix adaptations. However, these approaches destructs the structural integrity of the involved high-dimensional spaces. To tackle the diversity of dimensional spaces across different foundation models and provide a more precise representation of the changes within these spaces, this paper introduces a generalized parameter-efficient fine-tuning framework, designed for various dimensional parameter space. Specifically, our method asserts that changes in each dimensional parameter space are based on a low-rank core space which maintains the consistent topological structure with the original space. It then models the changes through this core space alongside corresponding weights to reconstruct alterations in the original space. It effectively preserves the structural integrity of the change of original N-dimensional parameter space, meanwhile models it via low-rank tensor adaptation. Extensive experiments on computer vision, natural language processing and multi-modal tasks validate the effectiveness of our method.

Problem

• Preserve structural integrity in high-dimensional parameter spaces
• Enable parameter-efficient fine-tuning across diverse dimensional spaces
• Model changes via low-rank core space with consistent topology

Innovation

• Generalized parameter-efficient fine-tuning framework
• Maintains structural integrity in high-dimensional spaces
• Utilizes low-rank tensor adaptation