  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.
