ARTiST：面向增强现实任务指导的自动化文本简化
ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality
AI总结：
本文提出 ARTiST 系统，利用少样本提示、GPT-3 与错误校准模型为 AR 头戴显示生成简化任务文本，实验证明其可显著降低认知负荷并提升任务表现。
摘要：
增强现实中呈现的文本为用户提供原位、实时的信息。然而，当用户从事认知要求较高的 AR 任务时，尤其是文本显示在头戴式显示器上时，这类内容可能难以快速理解。我们提出 ARTiST，这是一个自动文本简化系统，使用少样本提示和 GPT-3 模型，专门针对增强现实优化文本长度和语义内容。该系统源于一项包含 7 名用户和 3 名专家的形成性研究，将定制的错误校准模型与少样本提示相结合，以整合句法、词汇、详述和内容简化技术，并为头戴式显示器生成简化的 AR 文本。一项涉及 16 名用户的实证研究结果表明，与未修改文本以及通过传统方法修改的文本相比，ARTiST 显著减轻了认知负荷并提升了表现。我们的工作朝着在增强现实中自动化优化批量文本数据的可读性和性能迈出了一步。
英文摘要：
Text presented in augmented reality provides in-situ, real-time information for users. However, this content can be challenging to apprehend quickly when engaging in cognitively demanding AR tasks, especially when it is presented on a head-mounted display. We propose ARTiST, an automatic text simplification system that uses a few-shot prompt and GPT-3 models to specifically optimize the text length and semantic content for augmented reality. Developed out of a formative study that included seven users and three experts, our system combines a customized error calibration model with a few-shot prompt to integrate the syntactic, lexical, elaborative, and content simplification techniques, and generate simplified AR text for head-worn displays. Results from a 16-user empirical study showed that ARTiST lightens the cognitive load and improves performance significantly over both unmodified text and text modified via traditional methods. Our work constitutes a step towards automating the optimization of batch text data for readability and performance in augmented reality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
