2024/02/10 by Marcell Vazquez-Chanlatte, Karim Elmaaroufi, Vazquez-Chanlatte, Marcell +5 · 2 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Formal Languages and Automata Theory (cs.FL) #Machine Learning (cs.LG) #Machine Learning and Algorithms #Natural Language Processing Techniques #semigroups and automata theory
paper · pdf · doi:10.48550/arxiv.2402.07051
openalex publication_date 2024/02/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.