Add to ORKGPrompt-based models have gathered a lot of attention from researchers due to their remarkable advancements in the fields of zero-shot and few-shot learning. Developing an effective prompt template plays a critical role. However, prior studies have mainly focused on prompt vocabulary selection or embedding initialization within a predefined template with the prompt position fixed. In this empirical study, we conduct the most comprehensive analysis to date of prompt position for diverse natural language process tasks. Our findings quantify the substantial impact prompt position has on model performance. We observe that the prompt position used in prior studies is often sub-optimal. These findings suggest prompt position optimisation as a valu...
Natural language prompts have been shown to facilitate cross-task generalization for large language ...
In recent years, there has been significant progress in developing pre-trained language models for N...
Speech representations learned from Self-supervised learning (SSL) models can benefit various speech...
When primed with only a handful of training samples, very large, pretrained language models such as ...
Domain-specific text classification faces the challenge of scarce labeled data due to the high cost ...
Prompt tuning learns soft prompts to condition frozen Pre-trained Language Models (PLMs) for perform...
Probing Pre-trained Language Models (PLMs) using prompts has indirectly implied that language models...
Enhancing the zero-shot performance of instruction-following models requires heavy computation, eith...
Prompt-based learning has shown considerable promise in reformulating various downstream tasks as cl...
Prompts have been shown to be an effective method to adapt a frozen Pretrained Language Model (PLM) ...
Word order, an essential property of natural languages, is injected in Transformer-based neural lang...
Prompt learning is a new paradigm in the Natural Language Processing (NLP) field which has shown imp...
One of the most impressive results of recent NLP history is the ability of pre-trained language mode...
Recent works have shown that attaching prompts to the input is effective at conditioning Language Mo...
This paper presents AutoHint, a novel framework for automatic prompt engineering and optimization fo...
Natural language prompts have been shown to facilitate cross-task generalization for large language ...
In recent years, there has been significant progress in developing pre-trained language models for N...
Speech representations learned from Self-supervised learning (SSL) models can benefit various speech...
When primed with only a handful of training samples, very large, pretrained language models such as ...
Domain-specific text classification faces the challenge of scarce labeled data due to the high cost ...
Prompt tuning learns soft prompts to condition frozen Pre-trained Language Models (PLMs) for perform...
Probing Pre-trained Language Models (PLMs) using prompts has indirectly implied that language models...
Enhancing the zero-shot performance of instruction-following models requires heavy computation, eith...
Prompt-based learning has shown considerable promise in reformulating various downstream tasks as cl...
Prompts have been shown to be an effective method to adapt a frozen Pretrained Language Model (PLM) ...
Word order, an essential property of natural languages, is injected in Transformer-based neural lang...
Prompt learning is a new paradigm in the Natural Language Processing (NLP) field which has shown imp...
One of the most impressive results of recent NLP history is the ability of pre-trained language mode...
Recent works have shown that attaching prompts to the input is effective at conditioning Language Mo...
This paper presents AutoHint, a novel framework for automatic prompt engineering and optimization fo...
Natural language prompts have been shown to facilitate cross-task generalization for large language ...
In recent years, there has been significant progress in developing pre-trained language models for N...
Speech representations learned from Self-supervised learning (SSL) models can benefit various speech...