Add to ORKGWe present Generalized LoRA (GLoRA), an advanced approach for universal parameter-efficient fine-tuning tasks. Enhancing Low-Rank Adaptation (LoRA), GLoRA employs a generalized prompt module to optimize pre-trained model weights and adjust intermediate activations, providing more flexibility and capability across diverse tasks and datasets. Moreover, GLoRA facilitates efficient parameter adaptation by employing a scalable, modular, layer-wise structure search that learns individual adapter of each layer. Originating from a unified mathematical formulation, GLoRA exhibits strong transfer learning, few-shot learning and domain generalization abilities, as it adapts to new tasks through not only weights but also additional dimensions like acti...
Parameter-efficient fine-tuning (PEFT) has shown its effectiveness in adapting the pre-trained langu...
Standard fine-tuning of large pre-trained language models (PLMs) for downstream tasks requires updat...
Fine-tuning the entire set of parameters of a large pretrained model has become the mainstream appro...
In this paper, we present Delta-LoRA, which is a novel parameter-efficient approach to fine-tune lar...
Large language models (LLMs) and vision language models (VLMs) demonstrate excellent performance on ...
With the increasing prevalence of Large Language Models, traditional full fine-tuning approaches fac...
Existing fine-tuning methods either tune all parameters of the pre-trained model (full fine-tuning),...
Adapting large-scale pretrained models to various downstream tasks via fine-tuning is a standard met...
Adapting pretrained language models to novel domains, such as clinical applications, traditionally i...
Visual Parameter-Efficient Fine-Tuning (PEFT) has become a powerful alternative for full fine-tuning...
Parameter Efficient Tuning has been an prominent approach to adapt the Large Language Model to downs...
Parameter-efficient fine-tuning (PEFT) methods can adapt large language models to downstream tasks b...
Transformers have emerged as the state of the art neural network architecture for natural language p...
Pretrained Transformers achieve state-of-the-art performance in various code-processing tasks but ma...
Large-scale pre-trained language models have achieved impressive results on a wide range of downstre...
Parameter-efficient fine-tuning (PEFT) has shown its effectiveness in adapting the pre-trained langu...
Standard fine-tuning of large pre-trained language models (PLMs) for downstream tasks requires updat...
Fine-tuning the entire set of parameters of a large pretrained model has become the mainstream appro...
In this paper, we present Delta-LoRA, which is a novel parameter-efficient approach to fine-tune lar...
Large language models (LLMs) and vision language models (VLMs) demonstrate excellent performance on ...
With the increasing prevalence of Large Language Models, traditional full fine-tuning approaches fac...
Existing fine-tuning methods either tune all parameters of the pre-trained model (full fine-tuning),...
Adapting large-scale pretrained models to various downstream tasks via fine-tuning is a standard met...
Adapting pretrained language models to novel domains, such as clinical applications, traditionally i...
Visual Parameter-Efficient Fine-Tuning (PEFT) has become a powerful alternative for full fine-tuning...
Parameter Efficient Tuning has been an prominent approach to adapt the Large Language Model to downs...
Parameter-efficient fine-tuning (PEFT) methods can adapt large language models to downstream tasks b...
Transformers have emerged as the state of the art neural network architecture for natural language p...
Pretrained Transformers achieve state-of-the-art performance in various code-processing tasks but ma...
Large-scale pre-trained language models have achieved impressive results on a wide range of downstre...
Parameter-efficient fine-tuning (PEFT) has shown its effectiveness in adapting the pre-trained langu...
Standard fine-tuning of large pre-trained language models (PLMs) for downstream tasks requires updat...
Fine-tuning the entire set of parameters of a large pretrained model has become the mainstream appro...