Imagen de Portada

Reasoning and Sampling-Augmented MCQ Difficulty Prediction via LLMs

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Feng, Wanyong, Tran, Peter, Sireci, Stephen, Lan, Andrew
Formato:	Preprint
Publicado:	2025
Materias:	Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

From Text to Visuals: Using LLMs to Generate Math Diagrams with Vector Graphics
por: Lee, Jaewook, et al.
Publicado: (2025)

MCQ Difficulty Prediction via Modeling Learner Heterogeneity Using Data-Driven Cognitive Profiling
por: Krishnan, Dhriti, et al.
Publicado: (2026)

RADAR: Reasoning-Ability and Difficulty-Aware Routing for Reasoning LLMs
por: Fernandez, Nigel, et al.
Publicado: (2025)

Rectification Difficulty and Optimal Sample Allocation in LLM-Augmented Surveys
por: Ye, Zikun, et al.
Publicado: (2026)

Metric assessment protocol in the context of answer fluctuation on MCQ tasks
por: Goliakova, Ekaterina, et al.
Publicado: (2025)

LLaMa-SciQ: An Educational Chatbot for Answering Science MCQ
por: Allard, Marc-Antoine, et al.
Publicado: (2024)

AutoMCQ -- Automatically Generate Code Comprehension Questions using GenAI
por: Goodfellow, Martin, et al.
Publicado: (2025)

HS-STaR: Hierarchical Sampling for Self-Taught Reasoners via Difficulty Estimation and Budget Reallocation
por: Xiong, Feng, et al.
Publicado: (2025)

ABench-Physics: Benchmarking Physical Reasoning in LLMs via High-Difficulty and Dynamic Physics Problems
por: Zhang, Yiming, et al.
Publicado: (2025)

Optimizing Reasoning Efficiency through Prompt Difficulty Prediction
por: Zhao, Bo, et al.
Publicado: (2025)

Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues
por: Huang, Shuyan, et al.
Publicado: (2026)

AdaCtrl: Towards Adaptive and Controllable Reasoning via Difficulty-Aware Budgeting
por: Huang, Shijue, et al.
Publicado: (2025)

Can LLMs Estimate Student Struggles? Human-AI Difficulty Alignment with Proficiency Simulation for Item Difficulty Prediction
por: Li, Ming, et al.
Publicado: (2025)

Can Prompt Difficulty be Online Predicted for Accelerating RL Finetuning of Reasoning Models?
por: Qu, Yun, et al.
Publicado: (2025)

Rethinking the Sampling Criteria in Reinforcement Learning for LLM Reasoning: A Competence-Difficulty Alignment Perspective
por: Kong, Deyang, et al.
Publicado: (2025)

Zero-shot Graph Reasoning via Retrieval Augmented Framework with LLMs
por: Li, Hanqing, et al.
Publicado: (2025)

QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation
por: Li, Jiazheng, et al.
Publicado: (2025)

NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons
por: Romeo, Carlo, et al.
Publicado: (2025)

Temporal Sampling for Forgotten Reasoning in LLMs
por: Li, Yuetai, et al.
Publicado: (2025)

VADE: Variance-Aware Dynamic Sampling via Online Sample-Level Difficulty Estimation for Multimodal RL
por: Hu, Zengjie, et al.
Publicado: (2025)

MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning
por: Wang, Xukai, et al.
Publicado: (2025)

DIFFUMA: High-Fidelity Spatio-Temporal Video Prediction via Dual-Path Mamba and Diffusion Enhancement
por: Xie, Xinyu, et al.
Publicado: (2025)

Concise Reasoning, Big Gains: Pruning Long Reasoning Trace with Difficulty-Aware Prompting
por: Wu, Yifan, et al.
Publicado: (2025)

Mitigating Overthinking in Large Reasoning Models via Difficulty-aware Reinforcement Learning
por: Wan, Qian, et al.
Publicado: (2026)

Tailored Teaching with Balanced Difficulty: Elevating Reasoning in Multimodal Chain-of-Thought via Prompt Curriculum
por: Yang, Xinglong, et al.
Publicado: (2025)

Difficulty Estimation and Simplification of French Text Using LLMs
por: Jamet, Henri, et al.
Publicado: (2024)

DART: Difficulty-Adaptive Reasoning Truncation for Efficient Large Language Models
por: Zhang, Ruofan, et al.
Publicado: (2025)

Perception Graph for Cognitive Attack Reasoning in Augmented Reality
por: Chen, Rongqian, et al.
Publicado: (2025)

KAG-Thinker: Interactive Thinking and Deep Reasoning in LLMs via Knowledge-Augmented Generation
por: Zhang, Dalong, et al.
Publicado: (2025)

Online Difficulty Filtering for Reasoning Oriented Reinforcement Learning
por: Bae, Sanghwan, et al.
Publicado: (2025)

Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling
por: Bansal, Hritik, et al.
Publicado: (2024)

Reinforcing Numerical Reasoning in LLMs for Tabular Prediction via Structural Priors
por: Cai, Pengxiang, et al.
Publicado: (2025)

The Illusion of Reasoning: Exposing Evasive Data Contamination in LLMs via Zero-CoT Truncation
por: Lan, Yifan, et al.
Publicado: (2026)

DIVA-GRPO: Enhancing Multimodal Reasoning through Difficulty-Adaptive Variant Advantage
por: Gao, Haowen, et al.
Publicado: (2026)

Make Every Penny Count: Difficulty-Adaptive Self-Consistency for Cost-Efficient Reasoning
por: Wang, Xinglin, et al.
Publicado: (2024)

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

From Reasoning to Generalization: Knowledge-Augmented LLMs for ARC Benchmark
por: Lei, Chao, et al.
Publicado: (2025)

DAST: Difficulty-Adaptive Slow-Thinking for Large Reasoning Models
por: Shen, Yi, et al.
Publicado: (2025)

Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty
por: Cho, Yeseul, et al.
Publicado: (2025)

Do Persona-Infused LLMs Affect Performance in a Strategic Reasoning Game?
por: Licato, John, et al.
Publicado: (2025)