Premise Order Matters in Reasoning with Large Language Models
Authors: name: Xinyun Chen, name: Ryan A. Chi, name: Xuezhi Wang, name: Denny Zhou

Large language models (LLMs) have accomplished remarkable reasoning
performance in various domains. However, in the domain of reasoning tasks, we
discover a frailty: LLMs are surprisingly brittle to the ordering of the
premises, despite the fact that such ordering does not alter the underlying
task. In particular, we observe that LLMs achieve the best performance when the
premise order aligns with the context required in intermediate reasoning steps.
For example, in deductive reasoning tasks, presenting the premises in the same
order as the ground truth proof in the prompt (as opposed to random ordering)
drastically increases the model's accuracy. We first examine the effect of
premise ordering on deductive reasoning on a variety of LLMs, and our
evaluation shows that permuting the premise order can cause a performance drop
of over 30%. In addition, we release the benchmark R-GSM, based on GSM8K, to
examine the ordering effect for mathematical problem-solving, and we again
observe a significant drop in accuracy, relative to the original GSM8K
benchmark.