Cover Image

Question Difficulty Ranking for Multiple-Choice Reading Comprehension

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Raina, Vatsal, Gales, Mark
Format:	Preprint
Published:	2024
Subjects:	Computation and Language Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

An Information-Theoretic Approach to Analyze NLP Classification Tasks
by: Wang, Luran, et al.
Published: (2024)

Question-Based Retrieval using Atomic Units for Enterprise RAG
by: Raina, Vatsal, et al.
Published: (2024)

Finetuning LLMs for Comparative Assessment Tasks
by: Raina, Vatsal, et al.
Published: (2024)

Is it Possible to Modify Text to a Target Readability Level? An Initial Investigation Using Zero-Shot Large Language Models
by: Farajidizaji, Asma, et al.
Published: (2023)

RankLLM: Weighted Ranking of LLMs by Quantifying Question Difficulty
by: Zhang, Ziqian, et al.
Published: (2026)

Probing the Limits of Stylistic Alignment in Vision-Language Models
by: Farajidizaji, Asma, et al.
Published: (2025)

A Vietnamese Dataset for Text Segmentation and Multiple Choices Reading Comprehension
by: Hai, Toan Nguyen, et al.
Published: (2025)

Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension
by: Vatsal, Shubham, et al.
Published: (2024)

UnibucLLM: Harnessing LLMs for Automated Prediction of Item Difficulty and Response Time for Multiple-Choice Questions
by: Rogoz, Ana-Cristina, et al.
Published: (2024)

Differentiating Choices via Commonality for Multiple-Choice Question Answering
by: Deng, Wenqing, et al.
Published: (2024)

Efficient LLM Comparative Assessment: a Product of Experts Framework for Pairwise Comparisons
by: Liusie, Adian, et al.
Published: (2024)

Generating Multiple-Choice Knowledge Questions with Interpretable Difficulty Estimation using Knowledge Graphs and Large Language Models
by: Şakiroğlu, Mehmet Can, et al.
Published: (2026)

Plausibly Problematic Questions in Multiple-Choice Benchmarks for Commonsense Reasoning
by: Palta, Shramay, et al.
Published: (2024)

Question Generation for Assessing Early Literacy Reading Comprehension
by: Yang, Xiaocheng, et al.
Published: (2025)

Automated Generation and Tagging of Knowledge Components from Multiple-Choice Questions
by: Moore, Steven, et al.
Published: (2024)

Exploiting the English Vocabulary Profile for L2 word-level vocabulary assessment with LLMs
by: Bannò, Stefano, et al.
Published: (2025)

Automatic Generation of Inference Making Questions for Reading Comprehension Assessments
by: Ma, Wanjing Anya, et al.
Published: (2025)

Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction
by: Lee, Yooseop, et al.
Published: (2025)

Answer, Assemble, Ace: Understanding How LMs Answer Multiple Choice Questions
by: Wiegreffe, Sarah, et al.
Published: (2024)

Biomedical Entity Linking as Multiple Choice Question Answering
by: Lin, Zhenxi, et al.
Published: (2024)

Option-ID Based Elimination For Multiple Choice Questions
by: Zhu, Zhenhao, et al.
Published: (2025)

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights
by: Biancini, Giorgio, et al.
Published: (2025)

Conformal P-Value in Multiple-Choice Question Answering Tasks with Provable Risk Control
by: Ye, Yuanchang
Published: (2025)

Multiple Choice Learning of Low-Rank Adapters for Language Modeling
by: Letzelter, Victor, et al.
Published: (2025)

Balancing Rigor and Utility: Mitigating Cognitive Biases in Large Language Models for Multiple-Choice Questions
by: Zhong, Hanyang, et al.
Published: (2024)

Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models
by: Bao, Qiming, et al.
Published: (2023)

Grammatical Error Feedback: An Implicit Evaluation Approach
by: Bannò, Stefano, et al.
Published: (2024)

Pattern Recognition or Medical Knowledge? The Problem with Multiple-Choice Questions in Medicine
by: Griot, Maxime, et al.
Published: (2024)

It is Too Many Options: Pitfalls of Multiple-Choice Questions in Generative AI and Medical Education
by: Singh, Shrutika, et al.
Published: (2025)

Choices Speak Louder than Questions
by: Cho, Gyeongje, et al.
Published: (2025)

MCQG-SRefine: Multiple Choice Question Generation and Evaluation with Iterative Self-Critique, Correction, and Comparison Feedback
by: Yao, Zonghai, et al.
Published: (2024)

Conformal Sets in Multiple-Choice Question Answering under Black-Box Settings with Provable Coverage Guarantees
by: Yang, Guang, et al.
Published: (2025)

(WhyPHI) Fine-Tuning PHI-3 for Multiple-Choice Question Answering: Methodology, Results, and Challenges
by: Abdellatif, Mohamed Hisham
Published: (2025)

Automated Generation of Curriculum-Aligned Multiple-Choice Questions for Malaysian Secondary Mathematics Using Generative AI
by: Wahid, Rohaizah Abdul, et al.
Published: (2025)

Large Language Models in Fire Engineering: An Examination of Technical Questions Against Domain Knowledge
by: Hostetter, Haley, et al.
Published: (2024)

AraSTEM: A Native Arabic Multiple Choice Question Benchmark for Evaluating LLMs Knowledge In STEM Subjects
by: Mustapha, Ahmad, et al.
Published: (2024)

Cognitively Diverse Multiple-Choice Question Generation: A Hybrid Multi-Agent Framework with Large Language Models
by: Tian, Yu, et al.
Published: (2026)

Anchored Answers: Unravelling Positional Bias in GPT-2's Multiple-Choice Questions
by: Li, Ruizhe, et al.
Published: (2024)

A Study on Large Language Models' Limitations in Multiple-Choice Question Answering
by: Khatun, Aisha, et al.
Published: (2024)

Can GPT Improve the State of Prior Authorization via Guideline Based Automated Question Answering?
by: Vatsal, Shubham, et al.
Published: (2024)