@inproceedings{zhou-etal-2024-revisiting,
    title = "Revisiting the Self-Consistency Challenges in Multi-Choice Question Formats for Large Language Model Evaluation",
    author = "Zhou, Wenjie  and
      Wang, Qiang  and
      Xu, Mingzhou  and
      Chen, Ming  and
      Duan, Xiangyu",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1229",
    pages = "14103--14110",
    abstract = "Multi-choice questions (MCQ) are a common method for assessing the world knowledge of large language models (LLMs), demonstrated by benchmarks such as MMLU and C-Eval. However, recent findings indicate that even top-tier LLMs, such as ChatGPT and GPT4, might display inconsistencies when faced with slightly varied inputs. This raises concerns about the credibility of MCQ-based evaluations. To address this issue, we introduced three knowledge-equivalent question variants: option position shuffle, option label replacement, and conversion to a True/False format. We rigorously tested a range of LLMs, varying in model size (from 6B to 70B) and types{---}pretrained language model (PLM), supervised fine-tuning (SFT), and reinforcement learning from human feedback (RLHF). Our findings from MMLU and C-Eval revealed that accuracy for individual questions lacks robustness, particularly in smaller models ({\textless}30B) and PLMs. Consequently, we advocate that consistent accuracy may serve as a more reliable metric for evaluating and ranking LLMs.",
}

Multi-choice questions (MCQ) are a common method for assessing the world knowledge of large language models (LLMs), demonstrated by benchmarks such as MMLU and C-Eval. However, recent findings indicate that even top-tier LLMs, such as ChatGPT and GPT4, might display inconsistencies when faced with slightly varied inputs. This raises concerns about the credibility of MCQ-based evaluations. To address this issue, we introduced three knowledge-equivalent question variants: option position shuffle, option label replacement, and conversion to a True/False format. We rigorously tested a range of LLMs, varying in model size (from 6B to 70B) and types{---}pretrained language model (PLM), supervised fine-tuning (SFT), and reinforcement learning from human feedback (RLHF). Our findings from MMLU and C-Eval revealed that accuracy for individual questions lacks robustness, particularly in smaller models ({\textless}30B) and PLMs. Consequently, we advocate that consistent accuracy may serve as a more reliable metric for evaluating and ranking LLMs.
Zhou, Wenjie Wang, Qiang Xu, Mingzhou Chen, Ming Duan, Xiangyu
Revisiting the Self-Consistency Challenges in Multi-Choice Question Formats for Large Language Model Evaluation