Prompt tuning matches full model tuning performance on large language models while tuning only a small fraction of parameters and improves robustness to domain shifts.
An Empirical Analysis of Human-Bot Interaction on R eddit
3 Pith papers cite this work. Polarity classification is still indexing.
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Decomposing annotation tasks using centers from centering theory reduces aggregate inferential load via a degrees-of-freedom model and enables better sub-task allocation.
PortBERT releases two RoBERTa models for Portuguese that match or beat prior monolingual and multilingual models on translated GLUE/SuperGLUE tasks while reporting training and inference times.
citing papers explorer
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Task Decomposition for Efficient Annotation
Decomposing annotation tasks using centers from centering theory reduces aggregate inferential load via a degrees-of-freedom model and enables better sub-task allocation.
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PortBERT: Navigating the Depths of Portuguese Language Models
PortBERT releases two RoBERTa models for Portuguese that match or beat prior monolingual and multilingual models on translated GLUE/SuperGLUE tasks while reporting training and inference times.