arXiv AI
What Makes Something Hard(er)? Explaining Question Difficulty in Natural Language
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arXiv AI
Aug 6
Revisiting Generalization Across Difficulty Levels: It's Not So Easy
arXiv:2511. 21692v3 Announce Type: replace-cross Abstract: We investigate how well large language models (LLMs) generalize across different task difficulties, a key question for effective data curation and evaluation.
arXiv AI
Jun 29
Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction
arXiv:2606. 28186v1 Announce Type: cross Abstract: Predicting human item difficulty is central to educational assessment, where reliable estimates support fairness and effective test construction.
arXiv AI
Jul 17
Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation
arXiv:2607. 14109v1 Announce Type: cross Abstract: Probing the capabilities of Large Language Models (LLMs) and building robust solutions for Multiple-Choice Question Answering (MCQA) remain central challenges in natural language understanding.
arXiv Machine Learning
Aug 3
Can LLMs Really Understand Item Difficulty Levels? Implications for Automated Item Generation Using LLMs
arXiv:2607. 28634v1 Announce Type: cross Abstract: The estimation of item difficulty plays a key role in both formative assessment and large-scale high-stakes summative assessments.
arXiv AI
2d ago
Certainty Is Not Just Correctness: Rethinking Token-Level Certainty in LLM Reasoning
The paper investigates token‑level certainty as a proxy for correctness in large language models. It finds that certainty better predicts whether a model will answer a question correctly than it does whether a specific response is correct, and that certainty varies by token type and position. The authors show that using certainty early in generation to allocate responses and later to weight votes improves accuracy while dramatically cutting token cost.
arXiv Computation and Language
Sep 22
Assessing Readability with LLMs: The Role of Reasoning and Few-Shot Prompting
arXiv:2609.24650v1 Announce Type: new Abstract: Readability assessment is essential for tailoring texts to intended audiences across educational, healthcare, and information retrieval domains. Howeve...