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Found 9 papers, 8 papers with code
GPT Understands, Too
Prompting a pretrained language model with natural language patterns has been proved effective for natural language understanding (NLU).
CPM: A Large-scale Generative Chinese Pre-trained Language Model
However, applying GPT-3 to address Chinese NLP tasks is still challenging, as the training corpus of GPT-3 is primarily English, and the parameters are not publicly available.
NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework
Pretrained language models have become the standard approach for many NLP tasks due to strong performance, but they are very expensive to train.
FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding
The few-shot natural language understanding (NLU) task has attracted much recent attention.
FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning
Most previous methods for text data augmentation are limited to simple tasks and weak baselines.
Prompt-Based Metric Learning for Few-Shot NER
Few-shot named entity recognition (NER) targets generalizing to unseen labels and/or domains with few labeled examples.
Zero-Label Prompt Selection
Natural language prompts have been shown to facilitate cross-task generalization for large language models.
A Universal Discriminator for Zero-Shot Generalization
In the finetuning setting, our approach also achieves new state-of-the-art results on a wide range of NLP tasks, with only 1/4 parameters of previous methods.
On the Performance of Data Compression in Clustered Fog Radio Access Networks
The fog-radio-access-network (F-RAN) has been proposed to address the strict latency requirements, which offloads computation tasks generated in user equipments (UEs) to the edge to reduce the processing latency.
