论文标题
人类也学习：使用优化的人类输入更好的人类互动
Humans learn too: Better Human-AI Interaction using Optimized Human Inputs
论文作者
论文摘要
人类越来越依赖AI组件的系统。 AI社区通常将人类的投入视为给定，并仅优化AI模型。这种想法是单方面的，它忽略了人类也可以学习的事实。在这项工作中，人类输入将被优化，以更好地与AI模型进行更好的交互，同时保持模型固定。优化的输入伴随着有关如何创建它们的说明。它们允许人类节省时间并削减错误，同时保持对原始输入有限的更改。我们建议以迭代方式修改样品的连续和离散优化方法。我们的定量和定性评估，包括对不同手工生成的输入的人类研究，表明生成的提案导致错误率较低，需要更少的努力来创建，并且与原始样本只有适度的差异。
Humans rely more and more on systems with AI components. The AI community typically treats human inputs as a given and optimizes AI models only. This thinking is one-sided and it neglects the fact that humans can learn, too. In this work, human inputs are optimized for better interaction with an AI model while keeping the model fixed. The optimized inputs are accompanied by instructions on how to create them. They allow humans to save time and cut on errors, while keeping required changes to original inputs limited. We propose continuous and discrete optimization methods modifying samples in an iterative fashion. Our quantitative and qualitative evaluation including a human study on different hand-generated inputs shows that the generated proposals lead to lower error rates, require less effort to create and differ only modestly from the original samples.
