It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners

TL;DR: A unified framework to incorporate the event type specific prompts for supervised, few-shot, and zero-shot event detection and shows up to 24.3% F-score gain over the previous state- of-the-art baselines.

TL;DR: AmbigPrompt as discussed by the authors integrates an answering model with a prompting model in an iterative manner, which adaptively tracks the reading process and progressively triggers the answering model to compose distinct and relevant answers.

TL;DR: This paper proposes a knowledge-enhanced longformer by injecting three domain-specific knowledge: hierarchy, synonym, and abbreviation with additional pretraining using contrastive learning to address the long-tail challenge of automatic ICD coding.

TL;DR: This study proposes DART, a novel approach that reformulates NLP tasks as pre-trained language model tasks, differentially optimizing prompts and labels with backpropagation, achieving better few-shot performance on standard NLP tasks without prompt engineering.

TL;DR: A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.

Abstract: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.

TL;DR: It is found that BERT was significantly undertrained, and can match or exceed the performance of every model published after it, and the best model achieves state-of-the-art results on GLUE, RACE and SQuAD.

TL;DR: GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic.