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Found 10 papers, 4 papers with code
Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models
In this work, we adapt prompt-based few-shot learning to ELECTRA and show that it outperforms masked language models in a wide range of tasks.
ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection
Hate speech detection is complex; it relies on commonsense reasoning, knowledge of stereotypes, and an understanding of social nuance that differs from one culture to the next.
On the Role of Bidirectionality in Language Model Pre-Training
Prior work on language model pre-training has explored different architectures and learning objectives, but differences in data, hyperparameters and evaluation make a principled comparison difficult.
Efficient Language Modeling with Sparse all-MLP
All-MLP architectures have attracted increasing interest as an alternative to attention-based models.
Ranked #1 on
Zero-Shot Learning
on COPA
Efficient Large Scale Language Modeling with Mixtures of Experts
Mixture of Experts layers (MoEs) enable efficient scaling of language models through conditional computation.
Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning
Our proposed fine-tuning objective leads to models that are more robust to different levels of noise in the fine-tuning training data, and can generalize better to related tasks with limited labeled data.
Self-training Improves Pre-training for Natural Language Understanding
Unsupervised pre-training has led to much recent progress in natural language understanding.
Conversational Semantic Parsing
In this paper, we propose a semantic representation for such task-oriented conversational systems that can represent concepts such as co-reference and context carryover, enabling comprehensive understanding of queries in a session.
Preserving Integrity in Online Social Networks
Online social networks provide a platform for sharing information and free expression.
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token.
Ranked #5 on
Text Summarization
on X-Sum
