When primed with only a handful of training samples, very large, pretrained language models such as GPT-3 have shown competitive results when compared to fully-supervised, fine-tuned, large, pretrained language models. We demonstrate that the order in which the samples are provided can make the difference between near state-of-the-art and random guess performance: essentially some permutations are “fantastic” and some not. We analyse this phenomenon in detail, establishing that: it is present across model sizes (even for the largest current models), it is not related to a specific subset of samples, and that a given good permutation for one model is not transferable to another. While one could use a development set to determine which permut...
Pre-trained masked language models successfully perform few-shot learning by formulating downstream ...
We investigate the dynamics of increasing the number of model parameters versus the number of labele...
Recent few-shot methods, such as parameter-efficient fine-tuning (PEFT) and pattern exploiting train...
When scaled to hundreds of billions of parameters, pretrained language models such as GPT-3 (Brown e...
Domain-specific text classification faces the challenge of scarce labeled data due to the high cost ...
Prompt-based models have gathered a lot of attention from researchers due to their remarkable advanc...
Prompt tuning learns soft prompts to condition frozen Pre-trained Language Models (PLMs) for perform...
Recent prompt-based approaches allow pretrained language models to achieve strong performances on fe...
Prompt-based fine-tuning has boosted the performance of Pre-trained Language Models (PLMs) on few-sh...
Large Language models (LLMs) possess the capability to engage In-context Learning (ICL) by leveragin...
Prompt tuning has become a new paradigm for model tuning and it has demonstrated success in natural ...
Data augmentation techniques are widely used for enhancing the performance of machine learning model...
Large Language Model (LLM) has demonstrated significant ability in various Natural Language Processi...
Speech representations learned from Self-supervised learning (SSL) models can benefit various speech...
Natural language prompts have been shown to facilitate cross-task generalization for large language ...
Pre-trained masked language models successfully perform few-shot learning by formulating downstream ...
We investigate the dynamics of increasing the number of model parameters versus the number of labele...
Recent few-shot methods, such as parameter-efficient fine-tuning (PEFT) and pattern exploiting train...
When scaled to hundreds of billions of parameters, pretrained language models such as GPT-3 (Brown e...
Domain-specific text classification faces the challenge of scarce labeled data due to the high cost ...
Prompt-based models have gathered a lot of attention from researchers due to their remarkable advanc...
Prompt tuning learns soft prompts to condition frozen Pre-trained Language Models (PLMs) for perform...
Recent prompt-based approaches allow pretrained language models to achieve strong performances on fe...
Prompt-based fine-tuning has boosted the performance of Pre-trained Language Models (PLMs) on few-sh...
Large Language models (LLMs) possess the capability to engage In-context Learning (ICL) by leveragin...
Prompt tuning has become a new paradigm for model tuning and it has demonstrated success in natural ...
Data augmentation techniques are widely used for enhancing the performance of machine learning model...
Large Language Model (LLM) has demonstrated significant ability in various Natural Language Processi...
Speech representations learned from Self-supervised learning (SSL) models can benefit various speech...
Natural language prompts have been shown to facilitate cross-task generalization for large language ...
Pre-trained masked language models successfully perform few-shot learning by formulating downstream ...
We investigate the dynamics of increasing the number of model parameters versus the number of labele...
Recent few-shot methods, such as parameter-efficient fine-tuning (PEFT) and pattern exploiting train...