Graph-enhanced Large Language Models in Asynchronous Plan Reasoning

Reasoning about asynchronous plans is challenging since it requires sequential and parallel planning to optimize time costs. Can large language models (LLMs) succeed at this task? Here, we present the first large-scale study investigating this question. We find that a representative set of closed and open-source LLMs, including GPT-4 and LLaMA-2, behave poorly when not supplied with illustrations about the task-solving process in our benchmark AsyncHow. We propose a novel technique called Plan Like a Graph (PLaG) that combines graphs …

asynchronous costs cs.ai cs.cl cs.lg gpt gpt-4 graph language language models large language large language models llama llms planning question reasoning scale set study