Výsledky vyhledávání - "Bartezzaghi A"

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Prompting-based Synthetic Data Generation for Few-Shot Question Answering

Autor: Schmidt, Maximilian, Bartezzaghi, Andrea, Vu, Ngoc Thang

Although language models (LMs) have boosted the performance of Question Answering, they still need plenty of data. Data annotation, in contrast, is a time-consuming process. This especially applies to Question Answering, where possibly large document

Externí odkaz: http://arxiv.org/abs/2405.09335

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Combining Data Generation and Active Learning for Low-Resource Question Answering

Autor: Kimmich, Maximilian, Bartezzaghi, Andrea, Bogojeska, Jasmina, Malossi, Cristiano, Vu, Ngoc Thang

Neural approaches have become very popular in Question Answering (QA), however, they require a large amount of annotated data. In this work, we propose a novel approach that combines data augmentation via question-answer generation with Active Learni

Externí odkaz: http://arxiv.org/abs/2211.14880

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Enabling Reproducibility and Meta-learning Through a Lifelong Database of Experiments (LDE)

Autor: Tsay, Jason, Bartezzaghi, Andrea, Nolte, Aleke, Malossi, Cristiano

Artificial Intelligence (AI) development is inherently iterative and experimental. Over the course of normal development, especially with the advent of automated AI, hundreds or thousands of experiments are generated and are often lost or never exami

Externí odkaz: http://arxiv.org/abs/2202.10979

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