Linear Chain Transformation: Expanding Optimization Dynamics for Fine-Tuning Large Language Models

• URL: http://arxiv.org/abs/2411.00039v1
• Date: Tue, 29 Oct 2024 14:07:24 GMT
• Title: Linear Chain Transformation: Expanding Optimization Dynamics for Fine-Tuning Large Language Models
• Authors: Yulong Wang, Chang Zuo, Yin Xuan, Hong Li, Ni Wei,
• Abstract summary: Linear Chain Transformation (LinChain) is a novel approach that introduces a sequence of linear transformations during fine-tuning to enrich optimization dynamics. By incorporating multiple linear transformations into the parameter update process, LinChain expands the effective rank of updates and enhances the model's ability to learn complex task-specific representations. Our experiments on various benchmark tasks show that LinChain leads to better generalization, fewer learnable parameters, and improved task adaptation.
• Score: 11.314144876785823
• License:
• Abstract: Fine-tuning large language models (LLMs) has become essential for adapting pretrained models to specific downstream tasks. In this paper, we propose Linear Chain Transformation (LinChain), a novel approach that introduces a sequence of linear transformations during fine-tuning to enrich optimization dynamics. By incorporating multiple linear transformations into the parameter update process, LinChain expands the effective rank of updates and enhances the model's ability to learn complex task-specific representations. We demonstrate that this method significantly improves the performance of LLM fine-tuning over state-of-the-art methods by providing more flexible optimization paths during training, while maintaining the inference efficiency of the resulting model. Our experiments on various benchmark tasks show that LinChain leads to better generalization, fewer learnable parameters, and improved task adaptation, making it a compelling strategy for LLM fine-tuning.

Related papers

• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• Enhancing Robustness of Vision-Language Models through Orthogonality Learning and Self-Regularization [77.62516752323207]
  We introduce an orthogonal fine-tuning method for efficiently fine-tuning pretrained weights and enabling enhanced robustness and generalization. A self-regularization strategy is further exploited to maintain the stability in terms of zero-shot generalization of VLMs, dubbed OrthSR. For the first time, we revisit the CLIP and CoOp with our method to effectively improve the model on few-shot image classficiation scenario.
  arXiv  Detail & Related papers  (2024-07-11T10:35:53Z)
• Group and Shuffle: Efficient Structured Orthogonal Parametrization [3.540195249269228]
  We introduce a new class of structured matrices, which unifies and generalizes structured classes from previous works. We empirically validate our method on different domains, including adapting of text-to-image diffusion models and downstream task fine-tuning in language modeling.
  arXiv  Detail & Related papers  (2024-06-14T13:29:36Z)
• Large Language Models As Evolution Strategies [6.873777465945062]
  In this work, we investigate whether large language models (LLMs) are in principle capable of implementing evolutionary optimization algorithms. We introduce a novel prompting strategy, consisting of least-to-most sorting of discretized population members. We find that our setup allows the user to obtain an LLM-based evolution strategy, which we call EvoLLM', that robustly outperforms baseline algorithms.
  arXiv  Detail & Related papers  (2024-02-28T15:02:17Z)
• Unleashing the Potential of Large Language Models as Prompt Optimizers: An Analogical Analysis with Gradient-based Model Optimizers [108.72225067368592]
  We propose a novel perspective to investigate the design of large language models (LLMs)-based prompts. We identify two pivotal factors in model parameter learning: update direction and update method. In particular, we borrow the theoretical framework and learning methods from gradient-based optimization to design improved strategies.
  arXiv  Detail & Related papers  (2024-02-27T15:05:32Z)
• Uncovering mesa-optimization algorithms in Transformers [61.06055590704677]
  Some autoregressive models can learn as an input sequence is processed, without undergoing any parameter changes, and without being explicitly trained to do so. We show that standard next-token prediction error minimization gives rise to a subsidiary learning algorithm that adjusts the model as new inputs are revealed. Our findings explain in-context learning as a product of autoregressive loss minimization and inform the design of new optimization-based Transformer layers.
  arXiv  Detail & Related papers  (2023-09-11T22:42:50Z)
• Multiplicative update rules for accelerating deep learning training and increasing robustness [69.90473612073767]
  We propose an optimization framework that fits to a wide range of machine learning algorithms and enables one to apply alternative update rules. We claim that the proposed framework accelerates training, while leading to more robust models in contrast to traditionally used additive update rule.
  arXiv  Detail & Related papers  (2023-07-14T06:44:43Z)
• Meta-Learning Parameterized First-Order Optimizers using Differentiable Convex Optimization [13.043909705693249]
  We propose a meta-learning framework in which the inner loop optimization step involves solving a differentiable convex optimization (DCO) We illustrate the theoretical appeal of this approach by showing that it enables one-step optimization of a family of linear least squares problems.
  arXiv  Detail & Related papers  (2023-03-29T18:17:41Z)
• Transformer-Based Learned Optimization [37.84626515073609]
  We propose a new approach to learned optimization where we represent the computation's update step using a neural network. Our innovation is a new neural network architecture inspired by the classic BFGS algorithm. We demonstrate the advantages of our approach on a benchmark composed of objective functions traditionally used for the evaluation of optimization algorithms.
  arXiv  Detail & Related papers  (2022-12-02T09:47:08Z)
• Automatically Learning Compact Quality-aware Surrogates for Optimization Problems [55.94450542785096]
  Solving optimization problems with unknown parameters requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values. Recent work has shown that including the optimization problem as a layer in a complex training model pipeline results in predictions of iteration of unobserved decision making. We show that we can improve solution quality by learning a low-dimensional surrogate model of a large optimization problem.
  arXiv  Detail & Related papers  (2020-06-18T19:11:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.