Advancements in Parameter-Efficient Fine-Tuning for Large Language Models

The field of parameter-efficient fine-tuning (PEFT) for large language models (LLMs) is witnessing significant advancements aimed at enhancing model adaptability, computational efficiency, and task-specific performance. Recent developments focus on innovative approaches to optimize the fine-tuning process, reducing the computational and resource costs while maintaining or even improving model performance across a variety of tasks. Key trends include the introduction of adaptive and decomposed prompt tuning methods, the optimization of low-rank adaptation techniques, and the exploration of self-adaptive frameworks that dynamically adjust model parameters based on task requirements. These advancements are not only making LLMs more accessible by lowering the barriers to computational resources but are also pushing the boundaries of what these models can achieve in terms of adaptability and efficiency.

Noteworthy papers in this area include:

  • ADePT: Introduces an adaptive decomposed prompt tuning method that outperforms existing PEFT methods across a wide range of NLP tasks.
  • RoRA: Proposes a rank-adaptive reliability optimization method that enhances the performance of low-rank adaptation, especially in fine-tuning pruned models.
  • $\text{Transformer}^2$: Presents a self-adaptive framework for LLMs that dynamically adjusts model parameters for unseen tasks, offering a scalable solution for enhancing model adaptability.
  • Hi-DLR: Develops a Hessian-informed differential learning rate approach that improves convergence by dynamically determining learning rates during training.
  • TriAdaptLoRA: Introduces a brain-inspired triangular adaptive low-rank adaptation framework that dynamically optimizes the allocation of trainable parameters, achieving superior performance and stability.
  • LoRS: Offers an efficient low-rank adaptation method for sparse LLMs, reducing memory and computation consumption while maintaining performance.
  • Transformed Low-rank Adaptation via Tensor Decomposition: Proposes a new PEFT method that combines transform and residual adaptations to reduce the approximation gap and improve parameter efficiency in text-to-image models.

Sources

ADePT: Adaptive Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation

$\text{Transformer}^2$: Self-adaptive LLMs

A Hessian-informed hyperparameter optimization for differential learning rate

Optimizing Language Models for Grammatical Acceptability: A Comparative Study of Fine-Tuning Techniques

TriAdaptLoRA: Brain-Inspired Triangular Adaptive Low-Rank Adaptation for Parameter-Efficient Fine-Tuning

Comparative Analysis of Efficient Adapter-Based Fine-Tuning of State-of-the-Art Transformer Models

LoRS: Efficient Low-Rank Adaptation for Sparse Large Language Model

Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models

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