Question Difficulty Estimation for Large Language Models via Answer Plausibility Scoring

Researchers have developed Q-DAPS, a method that measures question difficulty for LLMs by analyzing the entropy of plausibility scores across candidate answers rather than relying on surface-level signals like readability or popularity. The approach addresses a gap in QA evaluation by capturing the actual reasoning complexity that modern models face. Validated across four major benchmarks (TriviaQA, NQ, MuSiQue, QASC), Q-DAPS offers a more nuanced lens for understanding where LLMs struggle, which has direct implications for dataset curation, model training, and comparative benchmarking in the field.

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Explainer

Q-DAPS sidesteps the brittleness of surface-level signals (readability, answer frequency) by measuring how confidently an LLM ranks plausible answers. The key insight is that genuine difficulty emerges when multiple answers seem equally plausible to the model, not when a question looks complex to humans.

This work sits alongside the May 12 difficulty assessment paper on predicting human-LLM disagreement. Both papers recognize that LLM-based evaluation requires calibration against what models actually find hard, not what humans assume they will. Q-DAPS provides the upstream signal (which questions are genuinely ambiguous to the model), while the disagreement prediction work flags when that signal diverges from human judgment. Together they form a more complete picture of LLM-as-a-Judge workflows for content curation and benchmarking.

If Q-DAPS entropy scores correlate with error rates on held-out test sets across all four benchmarks (TriviaQA, NQ, MuSiQue, QASC), that confirms the method captures real reasoning difficulty. If correlation breaks down on one benchmark, investigate whether that dataset has contamination or whether the method is sensitive to question type.

Coverage we drew on

Predicting Disagreement with Human Raters in LLM-as-a-Judge Difficulty Assessment without Using Generation-Time Probability Signals · arXiv cs.CL

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MentionsQ-DAPS · TriviaQA · NQ · MuSiQue · QASC

Read full story at arXiv cs.CL →(arxiv.org)

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