Parameter-Efficient Fine-Tuning of State Space Models

• URL: http://arxiv.org/abs/2410.09016v2
• Date: Fri, 14 Mar 2025 01:26:57 GMT
• Title: Parameter-Efficient Fine-Tuning of State Space Models
• Authors: Kevin Galim, Wonjun Kang, Yuchen Zeng, Hyung Il Koo, Kangwook Lee,
• Abstract summary: Deep State Space Models (SSMs) have become powerful tools for language modeling, offering high performance and linear scalability with sequence length.<n>This paper investigates the application of parameter-efficient fine-tuning (PEFT) methods to SSM-based models.<n>We propose Sparse Dimension Tuning (SDT), a PEFT method tailored for SSM modules.
• Score: 10.817729275974829
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep State Space Models (SSMs), such as Mamba (Gu & Dao, 2024), have become powerful tools for language modeling, offering high performance and linear scalability with sequence length. However, the application of parameter-efficient fine-tuning (PEFT) methods to SSM-based models remains underexplored. We start by investigating two fundamental questions on existing PEFT methods: (i) How do they perform on SSM-based models? (ii) Which parameters should they target for optimal results? Our analysis shows that LoRA and its variants consistently outperform all other PEFT methods. While LoRA is effective for linear projection matrices, it fails on SSM modules-yet still outperforms other methods applicable to SSMs, indicating their limitations. This underscores the need for a specialized SSM tuning approach. To address this, we propose Sparse Dimension Tuning (SDT), a PEFT method tailored for SSM modules. Combining SDT for SSMs with LoRA for linear projection matrices, we achieve state-of-the-art performance across extensive experiments.

Related papers

• Adaptive Pruning for Large Language Models with Structural Importance Awareness [66.2690963378878]
  Large language models (LLMs) have significantly improved language understanding and generation capabilities. LLMs are difficult to deploy on resource-constrained edge devices due to their high computational and storage resource demands. We propose structurally-aware adaptive pruning (SAAP) to significantly reduce the computational and memory costs while maintaining model performance.
  arXiv  Detail & Related papers  (2024-12-19T18:08:04Z)
• Recursive Learning of Asymptotic Variational Objectives [49.69399307452126]
  General state-space models (SSMs) are widely used in statistical machine learning and are among the most classical generative models for sequential time-series data. Online sequential IWAE (OSIWAE) allows for online learning of both model parameters and a Markovian recognition model for inferring latent states. This approach is more theoretically well-founded than recently proposed online variational SMC methods.
  arXiv  Detail & Related papers  (2024-11-04T16:12:37Z)
• Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs [75.11449420928139]
  Fine-tuning Large Language Models (LLMs) has become a crucial technique for adapting pre-trained models to downstream tasks. Low-Rank Adaptation (LoRA) has emerged as a promising solution, but there exists a gap between the practical performance of low-rank adaptations and its theoretical optimum. We propose eXtreme Gradient Boosting LoRA, a novel framework that bridges this gap by leveraging the power of ensemble learning.
  arXiv  Detail & Related papers  (2024-10-25T17:07:13Z)
• Controlled Low-Rank Adaptation with Subspace Regularization for Continued Training on Large Language Models [13.56631686493347]
  Large language models (LLMs) exhibit remarkable capabilities in natural language processing but face catastrophic forgetting when learning new tasks. We propose Controlled LoRA (CLoRA), a subspace regularization method on LoRA structure.
  arXiv  Detail & Related papers  (2024-10-22T08:27:23Z)
• Provable Benefits of Complex Parameterizations for Structured State Space Models [51.90574950170374]
  Structured state space models (SSMs) are linear dynamical systems adhering to a specified structure. In contrast to typical neural network modules, whose parameterizations are real, SSMs often use complex parameterizations. This paper takes a step towards explaining the benefits of complex parameterizations for SSMs by establishing formal gaps between real and complex diagonal SSMs.
  arXiv  Detail & Related papers  (2024-10-17T22:35:50Z)
• Zeroth-Order Fine-Tuning of LLMs in Random Subspaces [66.27334633749734]
  As language models grow in size, memory demands for backpropagation increase. Zeroth-order (ZOZO) optimization methods offer a memory-efficient alternative. We show that SubZero enhances fine-tuning and achieves faster results compared to standard ZOZO approaches.
  arXiv  Detail & Related papers  (2024-10-11T17:01:43Z)
• Unleashing the Power of Task-Specific Directions in Parameter Efficient Fine-tuning [65.31677646659895]
  This paper focuses on the concept of task-specific directions (TSDs)-critical for transitioning large models from pretrained states to task-specific enhancements in PEFT. We introduce a novel approach, LoRA-Dash, which aims to maximize the impact of TSDs during the fine-tuning process, thereby enhancing model performance on targeted tasks.
  arXiv  Detail & Related papers  (2024-09-02T08:10:51Z)
• Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendation [50.837277466987345]
  We focus on the field of large language models (LLMs) for recommendation. We propose RecLoRA, which incorporates a Personalized LoRA module that maintains independent LoRAs for different users. We also design a Few2Many Learning Strategy, using a conventional recommendation model as a lens to magnify small training spaces to full spaces.
  arXiv  Detail & Related papers  (2024-08-07T04:20:28Z)
• Enhancing Parameter Efficiency and Generalization in Large-Scale Models: A Regularized and Masked Low-Rank Adaptation Approach [10.980433187379868]
  Low-Rank Adaptation (LoRA) has been developed to reduce resource consumption while maintaining satisfactory fine-tuning results. This paper investigates the intrinsic dimension of the matrix updates approximated by the LoRA method and reveals the performance benefits of increasing this intrinsic dimension.
  arXiv  Detail & Related papers  (2024-07-16T15:26:31Z)
• PAFT: A Parallel Training Paradigm for Effective LLM Fine-Tuning [17.73193523921637]
  Large language models (LLMs) have shown remarkable abilities in diverse natural language processing (NLP) tasks. LLMs generally undergo supervised fine-tuning (SFT) followed by preference alignment to be usable in downstream applications. This paper introduces PAFT, a new PArallel training paradigm for effective LLM Fine-Tuning.
  arXiv  Detail & Related papers  (2024-06-25T20:11:37Z)
• SLMRec: Empowering Small Language Models for Sequential Recommendation [38.51895517016953]
  Sequential Recommendation task involves predicting the next item a user is likely to interact with, given their past interactions. Recent research demonstrates the great impact of LLMs on sequential recommendation systems. Due to the huge size of LLMs, it is inefficient and impractical to apply a LLM-based model in real-world platforms.
  arXiv  Detail & Related papers  (2024-05-28T07:12:06Z)
• The Expressive Capacity of State Space Models: A Formal Language Perspective [0.8948475969696075]
  recurrent models based on linear state space models (SSMs) have shown promising performance in language modeling (LM), competititve with transformers. We present a comprehensive theoretical study of the capacity of such SSMs as it compares to that of transformers and traditional RNNs.
  arXiv  Detail & Related papers  (2024-05-27T17:46:57Z)
• HOPE for a Robust Parameterization of Long-memory State Space Models [51.66430224089725]
  State-space models (SSMs) that utilize linear, time-invariant (LTI) systems are known for their effectiveness in learning long sequences. We develop a new parameterization scheme, called HOPE, for LTI systems that utilize Markov parameters within Hankel operators. Our new parameterization endows the SSM with non-decaying memory within a fixed time window, which is empirically corroborated by a sequential CIFAR-10 task with padded noise.
  arXiv  Detail & Related papers  (2024-05-22T20:20:14Z)
• MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies [85.57899012821211]
  Small Language Models (SLMs) are a resource-efficient alternative to Large Language Models (LLMs) We introduce MiniCPM, specifically the 1.2B and 2.4B non-embedding parameter variants. We also introduce MiniCPM family, including MiniCPM-DPO, MiniCPM-MoE and MiniCPM-128K.
  arXiv  Detail & Related papers  (2024-04-09T15:36:50Z)
• State Space Models as Foundation Models: A Control Theoretic Overview [3.3222241150972356]
  In recent years, there has been a growing interest in integrating linear state-space models (SSM) in deep neural network architectures. This paper is intended as a gentle introduction to SSM-based architectures for control theorists. It provides a systematic review of the most successful SSM proposals and highlights their main features from a control theoretic perspective.
  arXiv  Detail & Related papers  (2024-03-25T16:10:47Z)
• EfficientState Space Model viaFast Tensor Convolutionand Block Diagonalization [5.260841516691153]
  We propose a new state space layer based on multiple-input multiple-output SSM, called efficient SSM. Our eSSM is built on the convolutional representation of multi-input and multi-input (MIMO) SSM. In the model efficiency benchmark, the parameters of eSSM are only 12.89% of LSTM and 13.24% of Mamba.
  arXiv  Detail & Related papers  (2024-02-23T12:36:31Z)
• Scaling Pre-trained Language Models to Deeper via Parameter-efficient Architecture [68.13678918660872]
  We design a more capable parameter-sharing architecture based on matrix product operator (MPO) MPO decomposition can reorganize and factorize the information of a parameter matrix into two parts. Our architecture shares the central tensor across all layers for reducing the model size.
  arXiv  Detail & Related papers  (2023-03-27T02:34:09Z)
• When to Update Your Model: Constrained Model-based Reinforcement Learning [50.74369835934703]
  We propose a novel and general theoretical scheme for a non-decreasing performance guarantee of model-based RL (MBRL) Our follow-up derived bounds reveal the relationship between model shifts and performance improvement. A further example demonstrates that learning models from a dynamically-varying number of explorations benefit the eventual returns.
  arXiv  Detail & Related papers  (2022-10-15T17:57:43Z)

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.