论文标题
部分可观测时空混沌系统的无模型预测
Model Cascading: Towards Jointly Improving Efficiency and Accuracy of NLP Systems
论文作者
论文摘要
所有实例是否需要通过大型模型进行推断才能进行正确的预测？也许不是；某些实例很容易，即使是小容量模型也可以正确回答。这为提高系统计算效率提供了机会。在这项工作中，我们介绍了一项关于“级联模型”的探索性研究，这是一种简单的技术，它利用各种能力模型的集合来准确而有效地输出预测。通过在可用的级联模型数量不同的多个任务设置中进行的全面实验（k值），我们表明级联可以提高计算效率和预测准确性。例如，在k = 3设置中，级联节省高达88.93％的计算成本，并始终达到卓越的预测准确性，提高高达2.18％。我们还研究了在级联中引入其他模型的影响，并表明它进一步提高了效率的提高。最后，我们希望我们的工作能够促进有效的NLP系统的开发，从而使其在现实世界应用中广泛采用。
Do all instances need inference through the big models for a correct prediction? Perhaps not; some instances are easy and can be answered correctly by even small capacity models. This provides opportunities for improving the computational efficiency of systems. In this work, we present an explorative study on 'model cascading', a simple technique that utilizes a collection of models of varying capacities to accurately yet efficiently output predictions. Through comprehensive experiments in multiple task settings that differ in the number of models available for cascading (K value), we show that cascading improves both the computational efficiency and the prediction accuracy. For instance, in K=3 setting, cascading saves up to 88.93% computation cost and consistently achieves superior prediction accuracy with an improvement of up to 2.18%. We also study the impact of introducing additional models in the cascade and show that it further increases the efficiency improvements. Finally, we hope that our work will facilitate development of efficient NLP systems making their widespread adoption in real-world applications possible.
