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.

翻译：我们主张，大型语言模型（LLMs）可与形式化分析方法相结合，为人机交互（HRI）任务提供易于获取且具有相关性的反馈。虽然逻辑规约在定义和评估任务方面非常有用，但这些表征形式不易被非专家理解。幸运的是，LLMs擅长生成易于理解的文本来解释复杂概念。通过将任务评估结果及其他上下文信息整合到LLM提示中，我们能够有效地为学习者合成一套实用的改进建议，以提升其任务表现。