Machine learning often requires millions of examples to produce static, black-box models. In contrast, interactive task learning (ITL) emphasizes incremental knowledge acquisition from limited instruction provided by humans in modalities such as natural language. However, ITL systems often suffer from brittle, error-prone language parsing, which limits their usability. Large language models (LLMs) are resistant to brittleness but are not interpretable and cannot learn incrementally. We present VAL, an ITL system with a new philosophy for LLM/symbolic integration. By using LLMs only for specific tasks--such as predicate and argument selection--within an algorithmic framework, VAL reaps the benefits of LLMs to support interactive learning of hierarchical task knowledge from natural language. Acquired knowledge is human interpretable and generalizes to support execution of novel tasks without additional training. We studied users' interactions with VAL in a video game setting, finding that most users could successfully teach VAL using language they felt was natural.

翻译：机器学习通常需要数百万个示例才能生成静态的黑箱模型。相比之下，交互式任务学习（ITL）强调通过自然语言等人类交互模态，从有限的指导中逐步获取知识。然而，ITL系统常因脆弱且易出错的语义解析而受限，影响其可用性。大型语言模型（LLM）虽能克服脆弱性问题，但缺乏可解释性且无法进行增量学习。我们提出VAL系统，采用一种全新的LLM/符号系统融合哲学。通过将LLM仅用于算法框架内的特定任务（如谓词和论元选择），VAL充分利用LLM的优势，支持从自然语言中交互式学习层次化任务知识。所习得的知识具有人类可解释性，并能泛化到无需额外训练即可执行新任务。我们在视频游戏场景中研究了用户与VAL的交互，发现大多数用户能够使用自认为自然的语言成功教授VAL完成任务。