Automaton-Based Representations of Task Knowledge from Generative Language Models

• URL: http://arxiv.org/abs/2212.01944v5
• Date: Wed, 9 Aug 2023 21:55:36 GMT
• Title: Automaton-Based Representations of Task Knowledge from Generative Language Models
• Authors: Yunhao Yang, Jean-Rapha\"el Gaglione, Cyrus Neary, Ufuk Topcu
• Abstract summary: Large-scale generative language models (GLMs) can automatically generate relevant task knowledge. We propose a novel algorithm named GLM2FSA, which constructs a finite state automaton (FSA) encoding high-level task knowledge from a brief natural-language description of the task goal.
• Score: 24.63416209240575
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automaton-based representations of task knowledge play an important role in control and planning for sequential decision-making problems. However, obtaining the high-level task knowledge required to build such automata is often difficult. Meanwhile, large-scale generative language models (GLMs) can automatically generate relevant task knowledge. However, the textual outputs from GLMs cannot be formally verified or used for sequential decision-making. We propose a novel algorithm named GLM2FSA, which constructs a finite state automaton (FSA) encoding high-level task knowledge from a brief natural-language description of the task goal. GLM2FSA first sends queries to a GLM to extract task knowledge in textual form, and then it builds an FSA to represent this text-based knowledge. The proposed algorithm thus fills the gap between natural-language task descriptions and automaton-based representations, and the constructed FSA can be formally verified against user-defined specifications. We accordingly propose a method to iteratively refine the queries to the GLM based on the outcomes, e.g., counter-examples, from verification. We demonstrate GLM2FSA's ability to build and refine automaton-based representations of everyday tasks (e.g., crossing a road), and also of tasks that require highly-specialized knowledge (e.g., executing secure multi-party computation).

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