Multiple-Choice Questions are Efficient and Robust LLM Evaluators
Abstract: We present GSM-MC, a multiple-choice (MC) dataset constructed by collecting answers and incorrect predictions on GSM8K from 60 open-source models. Through extensive experiments, we show that LLMs' performance on the MC version of this popular benchmark is strongly correlated with their performance on the original version and is quite robust to distractor choices and option orders, while the evaluation time is reduced by a factor of up to 30. Following similar procedures, we introduce MATH-MC, constructed from MATH, and PythonIO, a new program reasoning MC dataset constructed from HumanEval and MBPP. Experimental results indicate that LLMs' performance on these MC benchmarks leaves much room for improvement.
Paper Type: Long
Research Area: Resources and Evaluation
Research Area Keywords: benchmarking, NLP datasets, evaluation methodologies, evaluation
Contribution Types: Data resources, Data analysis
Languages Studied: English
Submission Number: 2619
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