Title: Answer matching outperforms multiple choice for LLM evaluations
Primary area: datasets and benchmarks
Keywords: QA-benchmarks, evaluations

Abstract: Multiple choice benchmarks have long been the workhorse of language model evaluation because grading multiple choice is objective and easy to automate. However, we show multiple choice questions from popular benchmarks can often be answered without even seeing the question. These shortcuts arise from a fundamental limitation of discriminative evaluation not shared by evaluations of the model's free-form, generative answers. Until recently, there appeared to be no viable, scalable alternative to multiple choice---but, we show that this has changed. We consider generative evaluation via what we call answer matching: Give the candidate model the question without the options, have it generate a free-form response, then use a modern language model with the reference answer to determine if the response matches the reference. To compare the validity of different evaluation strategies, we measure agreement with human grading, by annotating responses to MMLU-Pro and GPQA-Diamond questions. We find answer matching using recent models--even small ones--achieves near-perfect agreement, in the range of inter-annotator agreement. In contrast, both multiple choice evaluation and using LLM-as-a-judge without reference answers aligns poorly with human grading. Improved evaluations via answer matching are not merely a conceptual concern---it reduces costs, and significantly changes model rankings. Multiple choice benchmarks that seem saturated start showing room for improvement when evaluated with answer matching. In light of these findings, we discuss how to move the evaluation ecosystem from multiple choice to answer matching.

TL;DR: We show multiple choice evaluations suffer from discriminative shortcuts, and grading generative responses via answer matching aligns better with human annotations.