Which of These Best Describes Multiple Choice Evaluation with LLMs? A)
  Forced B) Flawed C) Fixable D) All of the Above
Authors: name: Nishant Balepur, name: Rachel Rudinger, name: Jordan Lee Boyd-Graber

Multiple choice question answering (MCQA) is popular for LLM evaluation due
to its simplicity and human-like testing, but we argue for its reform. We first
reveal flaws in MCQA's format, as it struggles to: 1) test
generation/subjectivity; 2) match LLM use cases; and 3) fully test knowledge.
We instead advocate for generative formats based on human testing-where LLMs
construct and explain answers-better capturing user needs and knowledge while
remaining easy to score. We then show even when MCQA is a useful format, its
datasets suffer from: leakage; unanswerability; shortcuts; and saturation. In
each issue, we give fixes from education, like rubrics to guide MCQ writing;
scoring methods to bridle guessing; and Item Response Theory to build harder
MCQs. Lastly, we discuss LLM errors in MCQA-robustness, biases, and unfaithful
explanations-showing how our prior solutions better measure or address these
issues. While we do not need to desert MCQA, we encourage more efforts in
refining the task based on educational testing, advancing evaluations.