Low-Rank Adaptation with Task-Relevant Feature Enhancement for Fine-tuning Language Models

• URL: http://arxiv.org/abs/2412.09827v1
• Date: Fri, 13 Dec 2024 03:38:49 GMT
• Title: Low-Rank Adaptation with Task-Relevant Feature Enhancement for Fine-tuning Language Models
• Authors: Changqun Li, Chaofan Ding, Kexin Luan, Xinhan Di,
• Abstract summary: We propose Low-Rank Adaptation with Task-Relevant Feature Enhancement(LoRATRF) for enhancing task-relevant features from the perspective of editing neural network representations. As the experiments on a vareity of datasets including NLU, our method reduces 33.71% parameters and achieves better performance on a variety of datasets in comparison with SOTA low-rank methods.
• Score: 2.3963398306126864
• License:
• Abstract: Fine-tuning pre-trained large language models in a parameter-efficient manner is widely studied for its effectiveness and efficiency. LoRA is one of the most widely used methods, which assumes that the optimization process is essentially low dimensional. Although LoRA has demonstrated commendable performance, there remains a significant performance gap between LoRA and full fine-tuning when learning new tasks. In this work, we propose Low-Rank Adaptation with Task-Relevant Feature Enhancement(LoRATRF) for enhancing task-relevant features from the perspective of editing neural network representations. To prioritize task-relevant features, a task-aware filter that selectively extracts valuable knowledge from hidden representations for the target or current task is designed. As the experiments on a vareity of datasets including NLU, commonsense reasoning and mathematical reasoning tasks demonstrates, our method reduces 33.71% parameters and achieves better performance on a variety of datasets in comparison with SOTA low-rank methods.

Related papers

• RandLoRA: Full-rank parameter-efficient fine-tuning of large models [46.25124374446935]
  Low-Rank Adaptation (LoRA) and its variants have shown impressive results in reducing the number of trainable parameters and memory requirements of large transformer networks. However, the low-rank nature of the weight update inherently limits the representation power of fine-tuned models. This paper introduces RandLoRA, a method that performs full-rank updates using a learned linear combinations of low-rank, non-trainable random matrices.
  arXiv  Detail & Related papers  (2025-02-03T01:59:45Z)
• Retrieval Instead of Fine-tuning: A Retrieval-based Parameter Ensemble for Zero-shot Learning [22.748835458594744]
  We introduce Retrieval-based. Ensemble (RPE), a new method that creates a vectorized database of. Low-Rank Adaptations (LoRAs) RPE minimizes the need for extensive training and eliminates the requirement for labeled data, making it particularly effective for zero-shot learning. RPE is well-suited for privacy-sensitive domains like healthcare, as it modifies model parameters without accessing raw data.
  arXiv  Detail & Related papers  (2024-10-13T16:28:38Z)
• MTL-LoRA: Low-Rank Adaptation for Multi-Task Learning [74.43869839954168]
  We propose MTL-LoRA, which retains the advantages of low-rank adaptation while significantly enhancing multi-task learning capabilities. MTL-LoRA augments LoRA by incorporating additional task-adaptive parameters that differentiate task-specific information. This approach enables large language models (LLMs) pre-trained on general corpus to adapt to different target task domains with a limited number of trainable parameters.
  arXiv  Detail & Related papers  (2024-10-12T08:32:26Z)
• Flat-LoRA: Low-Rank Adaption over a Flat Loss Landscape [52.98187034726091]
  Low-Rank Adaptation (LoRA) is an efficient way to fine-tune models by optimizing only a low-rank matrix. A solution that appears flat in the LoRA space may exist sharp directions in the full parameter space, potentially harming generalization performance. We propose Flat-LoRA, an efficient approach that seeks a low-rank adaptation located in a flat region of the full parameter space.
  arXiv  Detail & Related papers  (2024-09-22T11:24:10Z)
• Parameter-Efficient Active Learning for Foundational models [7.799711162530711]
  Foundational vision transformer models have shown impressive few shot performance on many vision tasks. This research presents a novel investigation into the application of parameter efficient fine-tuning methods within an active learning (AL) framework.
  arXiv  Detail & Related papers  (2024-06-13T16:30:32Z)
• MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning [105.11844150736536]
  Low-rank adaptation is a popular parameter-efficient fine-tuning method for large language models. We propose a new method called MoRA, which employs a square matrix to achieve high-rank updating while maintaining the same number of trainable parameters. Our method outperforms LoRA on memory-intensive tasks and achieves comparable performance on other tasks.
  arXiv  Detail & Related papers  (2024-05-20T15:48:32Z)
• Instruction Matters: A Simple yet Effective Task Selection for Optimized Instruction Tuning of Specific Tasks [51.15473776489712]
  We introduce a simple yet effective task selection method that leverages instruction information alone to identify relevant tasks. Our method is significantly more efficient than traditional approaches, which require complex measurements of pairwise transferability between tasks or the creation of data samples for the target task. Experimental results demonstrate that training on a small set of tasks, chosen solely on the instructions, results in substantial improvements in performance on benchmarks such as P3, Big-Bench, NIV2, and Big-Bench Hard.
  arXiv  Detail & Related papers  (2024-04-25T08:49:47Z)
• LoRA-SP: Streamlined Partial Parameter Adaptation for Resource-Efficient Fine-Tuning of Large Language Models [7.926974917872204]
  LoRA-SP is a novel approach utilizing randomized half-selective parameter freezing. LoRA-SP significantly reduces computational and memory requirements without compromising model performance.
  arXiv  Detail & Related papers  (2024-02-28T06:50:10Z)
• MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-Tuning [71.50432879573614]
  Low-rank adaptation (LoRA) is based on the idea that the adaptation process is intrinsically low-dimensional. We present MELoRA, a mini-ensemble low-rank adapters that uses fewer trainable parameters while maintaining a higher rank. Our experimental results show that, compared to LoRA, MELoRA achieves better performance with 8 times fewer trainable parameters on natural language understanding tasks and 36 times fewer trainable parameters on instruction following tasks.
  arXiv  Detail & Related papers  (2024-02-27T07:14:12Z)
• Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning [81.3514358542452]
  Few-shot in-context learning (ICL) incurs substantial computational, memory, and storage costs because it involves processing all of the training examples every time a prediction is made. parameter-efficient fine-tuning offers an alternative paradigm where a small set of parameters are trained to enable a model to perform the new task. In this paper, we rigorously compare few-shot ICL and parameter-efficient fine-tuning and demonstrate that the latter offers better accuracy as well as dramatically lower computational costs.
  arXiv  Detail & Related papers  (2022-05-11T17:10:41Z)

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.