Question and answer generation is a data augmentation method that aims to improve question answering (QA) models given the limited amount of human labeled data. However, a considerable gap remains between synthetic and human-generated question-answer pairs. This work aims to narrow this gap by taking advantage of large language models and explores several factors such as model size, quality of pretrained models, scale of data synthesized, and algorithmic choices. On the SQUAD1.1 question answering task, we achieve higher accuracy using solely synthetic questions and answers than when using the SQUAD1.1 training set questions alone. Removing access to real Wikipedia data, we synthesize questions and answers from a synthetic text corpus generated by an 8.3 billion parameter GPT-2 model and achieve 88.4 Exact Match (EM) and 93.9 F1 score on the SQUAD1.1 dev set. We further apply our methodology to SQUAD2.0 and show a 2.8 absolute gain on EM score compared to prior work using synthetic data.
Current deep learning architectures are growing larger in order to learn from complex datasets. These architectures require giant matrix multiplication operations to train millions of parameters. Conversely, there is another growing trend to bring deep learning to low-power, embedded devices. The matrix operations, associated with both training and testing of deep networks, are very expensive from a computational and energy standpoint. We present a novel hashing based technique to drastically reduce the amount of computation needed to train and test deep networks. Our approach combines recent ideas from adaptive dropouts and randomized hashing for maximum inner product search to select the nodes with the highest activation efficiently. Our new algorithm for deep learning reduces the overall computational cost of forward and back-propagation by operating on significantly fewer (sparse) nodes. As a consequence, our algorithm uses only 5% of the total multiplications, while keeping on average within 1% of the accuracy of the original model. A unique property of the proposed hashing based back-propagation is that the updates are always sparse. Due to the sparse gradient updates, our algorithm is ideally suited for asynchronous and parallel training leading to near linear speedup with increasing number of cores. We demonstrate the scalability and sustainability (energy efficiency) of our proposed algorithm via rigorous experimental evaluations on several real datasets.
A wide range of watermarking evaluation approaches and especially image benchmarking suites have been described in the literature. Our paper sets the main focus on the evaluation of digital audio watermarking with StirMark Benchmark for Audio (SMBA). Here we describe the currently implemented single geometric attacks in detail and introduce our so-called attack profiles. Profiles reflect an application oriented point of view ranging from the normal usage of audio content like internet radio or music shops up to typical attacker scenarios. In particular we present a definition of an extended profile which is composed of three basic profiles specific for annotation watermarks. Furthermore, we demonstrate how SMBA attacks can be used to evaluate the transparency of digital watermarking algorithms regarding the embedding strength. Test results based on an example audio watermarking algorithm and the measurement of transparency and capacity are presented.
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