$L^*LM$: Learning Automata from Examples using Natural Language Oracles
Marcell Vazquez-Chanlatte Karim Elmaaroufi Stefan J. Witwicki Sanjit A. Seshia
Expert demonstrations have proven an easy way to indirectly specify complex
tasks. Recent algorithms even support extracting unambiguous formal
specifications, e.g. deterministic finite automata (DFA), from demonstrations.
Unfortunately, these techniques are generally not sample efficient. In this
work, we introduce $L^*LM$, an algorithm for learning DFAs from both
demonstrations and natural language. Due to the expressivity of natural
language, we observe a significant improvement in the data efficiency of
learning DFAs from expert demonstrations. Technically, $L^*LM$ leverages large
language models to answer membership queries about the underlying task. This is
then combined with recent techniques for transforming learning from
demonstrations into a sequence of labeled example learning problems. In our
experiments, we observe the two modalities complement each other, yielding a
powerful few-shot learner.