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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. Our data and code are available at https://github.com/Geralt-Targaryen/MC-Evaluation.","url_abs":"https://arxiv.org/abs/2405.11966v4","url_pdf":"https://arxiv.org/pdf/2405.11966v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multiple-choice-questions-are-efficient-and","repo_url":"https://github.com/geralt-targaryen/mc-evaluation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"gsm8k","task_name":"GSM8K"},{"task_slug":"humaneval","task_name":"HumanEval"},{"task_slug":"math","task_name":"Math"},{"task_slug":"multiple-choice","task_name":"Multiple-choice"},{"task_slug":null,"task_name":"mbpp"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2405.11966","atlas_url":"https://app.syntology.ai/?focus=2405.11966","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.11966"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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