WiCkeD: A Simple Method to Make Multiple Choice Benchmarks More Challenging

ACL ARR 2025 February Submission4693 Authors

Abstract: We introduce WiCkeD, a simple method to increase the complexity of existing multiple-choice benchmarks by randomly replacing a choice with "None of the above", a method often used in educational tests. We show that WiCkeD can be automatically applied to any existing benchmark, making it more challenging. We apply WiCkeD to 6 popular benchmarks and use it to evaluate 18 open-weight LLMs. The performance of the models drops12.1 points on average with respect to the original versions of the datasets. When using chainof-thought on 3 MMLU datasets, the performance drop for the WiCkeD variant is similar to the one observed when using the LLMs directly, showing that WiCkeD is also challenging for models with enhanced reasoning abilities. WiCkeD also uncovers that some models are more sensitive to the extra reasoning required, providing additional information with respect to the original benchmarks.We relase our code and data at github.com/anonymized.

Paper Type: Short

Research Area: Resources and Evaluation

Research Area Keywords: evaluation benchmarks, multiple choice questions, reasoning

Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models, Data resources

Languages Studied: English

Submission Number: 4693