Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities
Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …
P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks
Prompt tuning, which only tunes continuous prompts with a frozen language model,
substantially reduces per-task storage and memory usage at training. However, in the …
substantially reduces per-task storage and memory usage at training. However, in the …
[HTML][HTML] GPT understands, too
Prompting a pretrained language model with natural language patterns has been proved
effective for natural language understanding (NLU). However, our preliminary study reveals …
effective for natural language understanding (NLU). However, our preliminary study reveals …
Glue-x: Evaluating natural language understanding models from an out-of-distribution generalization perspective
Pre-trained language models (PLMs) are known to improve the generalization performance
of natural language understanding models by leveraging large amounts of data during the …
of natural language understanding models by leveraging large amounts of data during the …
A survey on stability of learning with limited labelled data and its sensitivity to the effects of randomness
Learning with limited labelled data, such as prompting, in-context learning, fine-tuning, meta-
learning, or few-shot learning, aims to effectively train a model using only a small amount of …
learning, or few-shot learning, aims to effectively train a model using only a small amount of …
Flex: Unifying evaluation for few-shot nlp
Few-shot NLP research is highly active, yet conducted in disjoint research threads with
evaluation suites that lack challenging-yet-realistic testing setups and fail to employ careful …
evaluation suites that lack challenging-yet-realistic testing setups and fail to employ careful …
What are the best systems? new perspectives on nlp benchmarking
Abstract In Machine Learning, a benchmark refers to an ensemble of datasets associated
with one or multiple metrics together with a way to aggregate different systems …
with one or multiple metrics together with a way to aggregate different systems …
LINGUIST: Language model instruction tuning to generate annotated utterances for intent classification and slot tagging
We present LINGUIST, a method for generating annotated data for Intent Classification and
Slot Tagging (IC+ ST), via fine-tuning AlexaTM 5B, a 5-billion-parameter multilingual …
Slot Tagging (IC+ ST), via fine-tuning AlexaTM 5B, a 5-billion-parameter multilingual …
Zero-and few-shot nlp with pretrained language models
The ability to efficiently learn from little-to-no data is critical to applying NLP to tasks where
data collection is costly or otherwise difficult. This is a challenging setting both academically …
data collection is costly or otherwise difficult. This is a challenging setting both academically …
MEAL: Stable and active learning for few-shot prompting
Few-shot classification has made great strides due to foundation models that, through
priming and prompting, are highly effective few-shot learners. However, this approach has …
priming and prompting, are highly effective few-shot learners. However, this approach has …