We address the general task of structured commonsense reasoning: given a
natural language input, the goal is to generate a graph such as an event -- or
a reasoning-graph. To employ large language models (LMs) for this task,
existing approaches ``serialize'' the output graph as a flat list of nodes and
edges. Although feasible, these serialized graphs strongly deviate from the
natural language corpora that LMs were pre-trained on, hindering LMs from
generating them correctly. In this paper, we show that when we instead frame
structured commonsense reasoning tasks as code generation tasks, pre-trained
LMs of code are better structured commonsense reasoners than LMs of natural
language, even when the downstream task does not involve source code at all. We
demonstrate our approach across three diverse structured commonsense reasoning
tasks. In all these natural language tasks, we show that using our approach, a
code generation LM (CODEX) outperforms natural-LMs that are fine-tuned on the
target task (e.g., T5) and other strong LMs such as GPT-3 in the few-shot
setting.

本文展示了通过将结构化常识推推理任务框架化为代码生成任务，使用代码生成语言模型（CODEX）可以在自然语言任务上优于 T5 和 GPT-3 等先进语言模型，即使任务并不涉及源代码。