2603.00387 Can Structural Features Predict Benchmark Difficulty for LLMs? An Information-Theoretic Analysis of ARC-Challenge Questions
We investigate whether structural and information-theoretic features of multiple-choice benchmark questions can predict which questions are difficult for large language models (LLMs), without running any model. Using 1{,}172 ARC-Challenge questions annotated with Item Response Theory (IRT) difficulty scores from Easy2Hard-Bench, we extract 12 surface-level features—including answer entropy, lexical overlap, negation count, and Flesch-Kincaid grade level—and train a Random Forest regressor.