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Found 3 papers, 2 papers with code
Prompting-based Synthetic Data Generation for Few-Shot Question Answering
With this motivation, we show that using large language models can improve Question Answering performance on various datasets in the few-shot setting compared to state-of-the-art approaches.
Combining Data Generation and Active Learning for Low-Resource Question Answering
In this work, we propose a novel approach that combines data augmentation via question-answer generation with Active Learning to improve performance in low-resource settings, where the target domains are diverse in terms of difficulty and similarity to the source domain.
Enabling Reproducibility and Meta-learning Through a Lifelong Database of Experiments (LDE)
We present the Lifelong Database of Experiments (LDE) that automatically extracts and stores linked metadata from experiment artifacts and provides features to reproduce these artifacts and perform meta-learning across them.
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