Rule or Story, Which is a Better Commonsense Expression for Talking with
  Large Language Models?
Ning Bian Xianpei Han Hongyu Lin Yaojie Lu Ben He Le Sun
Building machines with commonsense has been a longstanding challenge in NLP
due to the reporting bias of commonsense rules and the exposure bias of
rule-based commonsense reasoning. In contrast, humans convey and pass down
commonsense implicitly through stories. This paper investigates the inherent
commonsense ability of large language models (LLMs) expressed through
storytelling. We systematically investigate and compare stories and rules for
retrieving and leveraging commonsense in LLMs. Experimental results on 28
commonsense QA datasets show that stories outperform rules as the expression
for retrieving commonsense from LLMs, exhibiting higher generation confidence
and commonsense accuracy. Moreover, stories are the more effective commonsense
expression for answering questions regarding daily events, while rules are more
effective for scientific questions. This aligns with the reporting bias of
commonsense in text corpora. We further show that the correctness and relevance
of commonsense stories can be further improved via iterative self-supervised
fine-tuning. These findings emphasize the importance of using appropriate
language to express, retrieve, and leverage commonsense for LLMs, highlighting
a promising direction for better exploiting their commonsense abilities.