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Found 7 papers, 4 papers with code
HyperPrompt: Prompt-based Task-Conditioning of Transformers
Prompt-Tuning is a new paradigm for finetuning pre-trained language models in a parameter-efficient way.
ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning
Despite the recent success of multi-task learning and transfer learning for natural language processing (NLP), few works have systematically studied the effect of scaling up the number of tasks during pre-training.
Are Pretrained Convolutions Better than Pretrained Transformers?
In the context of language models, are convolutional models competitive to Transformers when pre-trained?
How Reliable are Model Diagnostics?
In the pursuit of a deeper understanding of a model's behaviour, there is recent impetus for developing suites of probes aimed at diagnosing models beyond simple metrics like accuracy or BLEU.
Are Pre-trained Convolutions Better than Pre-trained Transformers?
In the context of language models, are convolutional models competitive to Transformers when pre-trained?
Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS
In this work, we address this knowledge gap by developing deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data based on the Columbia Suicide Severity Rating Scale (C-SSRS).
OmniNet: Omnidirectional Representations from Transformers
In OmniNet, instead of maintaining a strictly horizontal receptive field, each token is allowed to attend to all tokens in the entire network.
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