Language models have been shown to perform remarkably well on a wide range of
natural language processing tasks. In this paper, we propose a novel system
that uses language models to perform multi-step logical reasoning. Our system
incorporates explicit planning into its inference procedure, thus able to make
more informed reasoning decisions at each step by looking ahead into their
future effects. In our experiments, our full system significantly outperforms
other competing systems. On a multiple-choice question answering task, our
system performs competitively compared to GPT-3-davinci despite having only
around 1.5B parameters. We conduct several ablation studies to demonstrate that
explicit planning plays a crucial role in the system's performance.

在本文中，我们提出了一个使用语言模型进行多步逻辑推理的新型系统，该系统将显式计划纳入其推理过程中，从而通过向前看到它们的未来效果，能够在每一步上做出更明智的推理决策。在我们的实验中，我们的完整系统明显优于其他竞争系统，在多项选择题回答任务中，我们的系统表现与 GPT-3-davinci 相当，尽管只有约 15B 的参数。我们进行了几项削减研究，以证明显式计划在系统性能中起着重要作用。