Graph-enhanced Large Language Models in Asynchronous Plan Reasoning
Fangru Lin Emanuele La Malfa Valentin Hofmann Elle Michelle Yang Anthony Cohn Janet B. Pierrehumbert
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 with natural language prompts and achieves state-of-the-art
results. We show that although PLaG can boost model performance, LLMs still
suffer from drastic degradation when task complexity increases, highlighting
the limits of utilizing LLMs for simulating digital devices. We see our study
as an exciting step towards using LLMs as efficient autonomous agents.