Multiple choice questions (MCQs) are a popular and important task for evaluating large language models (LLMs). Based on common strategies people use when answering MCQs, the process of elimination (PoE) has been proposed as an effective problem-solving method. Existing methods to the PoE generally fall into two categories: one involves having the LLM directly select the incorrect options, while the other involves scoring the options. However, both methods incur high computational costs and often perform worse than methods that directly answer the MCQs with the option IDs. To address this issue, this paper proposes a PoE based on option ID. Specifically, our method eliminates option by selecting the option ID with the lowest probability. We conduct experiments with 10 different LLMs in zero-shot settings on 7 publicly available datasets. The experimental results demonstrate that our method significantly improves the LLM's performance. Further analysis reveals that the sequential elimination strategy can effectively enhance the LLM's reasoning ability. Additionally, we find that sequential elimination is also applicable to few-shot settings and can be combined with debias methods to further improve LLM's performance.
View on arXiv@article{zhu2025_2501.15175,
title={ Option-ID Based Elimination For Multiple Choice Questions },
author={ Zhenhao Zhu and Bulou Liu and Qingyao Ai and Yiqun Liu },
journal={arXiv preprint arXiv:2501.15175},
year={ 2025 }
}