Large Language Models Enable Automated Formative Feedback in Human-Robot Interaction Tasks

We claim that LLMs can be paired with formal analysis methods to provide accessible, relevant feedback for HRI tasks. While logic specifications are useful for defining and assessing a task, these representations are not easily interpreted by non-experts. Luckily, LLMs are adept at generating easy-to-understand text that explains difficult concepts. By integrating task assessment outcomes and other contextual information into an LLM prompt, we can effectively synthesize a useful set of recommendations for the learner to improve their performance.

我们声称可以将LLM与正式分析方法配对，以提供有关HRI任务的可访问，相关的反馈。虽然逻辑规范可用于定义和评估任务，但这些表示不容易被非专家解释。幸运的是，LLM擅长生成易于理解的文本，这些文本解释了困难的概念。通过将任务评估结果和其他上下文信息集成到LLM提示中，我们可以有效地为学习者提高其表现的有用建议。