Large Language Models Sensitivity to The Order of Options in
  Multiple-Choice Questions
Pouya Pezeshkpour Estevam Hruschka
Large Language Models (LLMs) have demonstrated remarkable capabilities in
various NLP tasks. However, previous works have shown these models are
sensitive towards prompt wording, and few-shot demonstrations and their order,
posing challenges to fair assessment of these models. As these models become
more powerful, it becomes imperative to understand and address these
limitations. In this paper, we focus on LLMs robustness on the task of
multiple-choice questions -- commonly adopted task to study reasoning and
fact-retrieving capability of LLMs. Investigating the sensitivity of LLMs
towards the order of options in multiple-choice questions, we demonstrate a
considerable performance gap of approximately 13% to 75% in LLMs on different
benchmarks, when answer options are reordered, even when using demonstrations
in a few-shot setting. Through a detailed analysis, we conjecture that this
sensitivity arises when LLMs are uncertain about the prediction between the
top-2/3 choices, and specific options placements may favor certain prediction
between those top choices depending on the question caused by positional bias.
We also identify patterns in top-2 choices that amplify or mitigate the model's
bias toward option placement. We found that for amplifying bias, the optimal
strategy involves positioning the top two choices as the first and last
options. Conversely, to mitigate bias, we recommend placing these choices among
the adjacent options. To validate our conjecture, we conduct various
experiments and adopt two approaches to calibrate LLMs' predictions, leading to
up to 8 percentage points improvement across different models and benchmarks.