Add to ORKGWe propose structured prompt tuning, a simple and effective method to improve prompt tuning. Instead of prepending a sequence of tunable embeddings to the input, we generate the soft prompt embeddings through a hypernetwork. Our approach subsumes the standard prompt tuning, allows more flexibility in model design and can be applied to both single-task and multi-task training settings. Empirically, structured prompt tuning shows a gain of +1.2$~1.5 points on the GLUE benchmark and is less sensitive to the change of learning rate, compared to standard prompt tuning
The advent of hyper-scale and general-purpose pre-trained models is shifting the paradigm of buildin...
Enhancing the zero-shot performance of instruction-following models requires heavy computation, eith...
Recent few-shot learning methods, such as parameter-efficient fine-tuning (PEFT) and pattern exploit...
Prompt tuning (PT) is an effective approach to adapting pre-trained language models to downstream ta...
Prompt-Tuning is a new paradigm for finetuning pre-trained language models in a parameter-efficient ...
Recent studies show that prompt tuning can better leverage the power of large language models than f...
Prompt tuning has become a new paradigm for model tuning and it has demonstrated success in natural ...
Prompt Tuning (PT) has been largely successful as a parameter-efficient way of conditioning large-sc...
Prompt tuning learns soft prompts to condition frozen Pre-trained Language Models (PLMs) for perform...
Prompt-based fine-tuning has boosted the performance of Pre-trained Language Models (PLMs) on few-sh...
In recent years, prompt tuning has sparked a research surge in adapting pre-trained models. Unlike t...
Recent works have shown promising results of prompt tuning in stimulating pre-trained language model...
This paper explores the effectiveness of prompt programming in the fine-tuning process of a Hungaria...
The current modus operandi in adapting pre-trained models involves updating all the backbone paramet...
Speech representations learned from Self-supervised learning (SSL) models can benefit various speech...
The advent of hyper-scale and general-purpose pre-trained models is shifting the paradigm of buildin...
Enhancing the zero-shot performance of instruction-following models requires heavy computation, eith...
Recent few-shot learning methods, such as parameter-efficient fine-tuning (PEFT) and pattern exploit...
Prompt tuning (PT) is an effective approach to adapting pre-trained language models to downstream ta...
Prompt-Tuning is a new paradigm for finetuning pre-trained language models in a parameter-efficient ...
Recent studies show that prompt tuning can better leverage the power of large language models than f...
Prompt tuning has become a new paradigm for model tuning and it has demonstrated success in natural ...
Prompt Tuning (PT) has been largely successful as a parameter-efficient way of conditioning large-sc...
Prompt tuning learns soft prompts to condition frozen Pre-trained Language Models (PLMs) for perform...
Prompt-based fine-tuning has boosted the performance of Pre-trained Language Models (PLMs) on few-sh...
In recent years, prompt tuning has sparked a research surge in adapting pre-trained models. Unlike t...
Recent works have shown promising results of prompt tuning in stimulating pre-trained language model...
This paper explores the effectiveness of prompt programming in the fine-tuning process of a Hungaria...
The current modus operandi in adapting pre-trained models involves updating all the backbone paramet...
Speech representations learned from Self-supervised learning (SSL) models can benefit various speech...
The advent of hyper-scale and general-purpose pre-trained models is shifting the paradigm of buildin...
Enhancing the zero-shot performance of instruction-following models requires heavy computation, eith...
Recent few-shot learning methods, such as parameter-efficient fine-tuning (PEFT) and pattern exploit...