New method explains natural language question difficulty using LLMs

Researchers have developed a novel data-driven method to automatically generate and validate natural-language explanations for why certain questions are more difficult than others. This approach utilizes Item Response Theory to estimate question difficulty based on responses from a large pool of LLMs. By contrasting easy and hard questions, the system proposes and validates hypotheses about the underlying factors contributing to difficulty. The generated hypotheses are interpretable, predictive of question difficulty, and can even be used to causally shift a question's measured difficulty, offering a more actionable understanding beyond simple difficulty scores. AI

IMPACT Provides a more interpretable and actionable understanding of question difficulty for LLM evaluation and development.

RANK_REASON Academic paper detailing a new method for explaining question difficulty. [lever_c_demoted from research: ic=1 ai=1.0]

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