Imagen de Portada

Reasoning Models are Test Exploiters: Rethinking Multiple-Choice

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Raman, Narun, Lundy, Taylor, Leyton-Brown, Kevin
Formato:	Preprint
Publicado:	2025
Materias:	Computation and Language
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

QuickScope: Certifying Hard Questions in Dynamic LLM Benchmarks
por: Lundy, Taylor, et al.
Publicado: (2026)

STEER-ME: Assessing the Microeconomic Reasoning of Large Language Models
por: Raman, Narun, et al.
Publicado: (2025)

STEER: Assessing the Economic Rationality of Large Language Models
por: Raman, Narun, et al.
Publicado: (2024)

NFTs as a Data-Rich Test Bed: Conspicuous Consumption and its Determinants
por: Lundy, Taylor, et al.
Publicado: (2025)

Test-Time Reasoners Are Strategic Multiple-Choice Test-Takers
por: Balepur, Nishant, et al.
Publicado: (2025)

Understanding Understanding: A Pragmatic Framework Motivated by Large Language Models
por: Leyton-Brown, Kevin, et al.
Publicado: (2024)

Rethinking Multiple-Choice Questions for RLVR: Unlocking Potential via Distractor Design
por: Guo, Xu, et al.
Publicado: (2026)

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

Boosting Process-Correct CoT Reasoning by Modeling Solvability of Multiple-Choice QA
por: Schumann, Raphael, et al.
Publicado: (2025)

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

Rethinking the Outlier Distribution in Large Language Models: An In-depth Study
por: Raman, Rahul, et al.
Publicado: (2025)

Hypothesis Testing for Quantifying LLM-Human Misalignment in Multiple Choice Settings
por: Hong, Harbin, et al.
Publicado: (2025)

Large Language Models Are Not Robust Multiple Choice Selectors
por: Zheng, Chujie, et al.
Publicado: (2023)

When Choices Become Risks: Safety Failures of Large Language Models under Multiple-Choice Constraints
por: Chen, Yuheng, et al.
Publicado: (2026)

REST: Stress Testing Large Reasoning Models by Asking Multiple Problems at Once
por: Pan, Zhuoshi, et al.
Publicado: (2025)

Hierarchical Vision-Language Reasoning for Multimodal Multiple-Choice Question Answering
por: Zhou, Ao, et al.
Publicado: (2025)

Graph Laplacian Wavelet Transformer via Learnable Spectral Decomposition
por: Kiruluta, Andrew, et al.
Publicado: (2025)

UBench: Benchmarking Uncertainty in Large Language Models with Multiple Choice Questions
por: Wang, Xunzhi, et al.
Publicado: (2024)

GeoChallenge: A Multi-Answer Multiple-Choice Benchmark for Geometric Reasoning with Diagrams
por: Zhang, Yushun, et al.
Publicado: (2026)

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

None of the Others: a General Technique to Distinguish Reasoning from Memorization in Multiple-Choice LLM Evaluation Benchmarks
por: Salido, Eva Sánchez, et al.
Publicado: (2025)

Think Twice: Branch-and-Rethink Reasoning Reward Model
por: Jiao, Yizhu, et al.
Publicado: (2025)

Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident, Especially When They are Wrong
por: Fu, Tairan, et al.
Publicado: (2025)

Multiple-Choice Questions are Efficient and Robust LLM Evaluators
por: Zhang, Ziyin, et al.
Publicado: (2024)

The Role of the Availability Heuristic in Multiple-Choice Answering Behaviour
por: Zotos, Leonidas, et al.
Publicado: (2026)

Refract ICL: Rethinking Example Selection in the Era of Million-Token Models
por: Akula, Arjun R., et al.
Publicado: (2025)

ViMultiChoice: Toward a Method That Gives Explanation for Multiple-Choice Reading Comprehension in Vietnamese
por: Cao, Trung Tien, et al.
Publicado: (2026)

Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation
por: Alhazmi, Elaf, et al.
Publicado: (2024)

Math Multiple Choice Question Generation via Human-Large Language Model Collaboration
por: Lee, Jaewook, et al.
Publicado: (2024)

Can Model Uncertainty Function as a Proxy for Multiple-Choice Question Item Difficulty?
por: Zotos, Leonidas, et al.
Publicado: (2024)

Strengthened Symbol Binding Makes Large Language Models Reliable Multiple-Choice Selectors
por: Xue, Mengge, et al.
Publicado: (2024)

MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation
por: Lee, Dongjun, et al.
Publicado: (2024)

FourierNAT: A Fourier-Mixing-Based Non-Autoregressive Transformer for Parallel Sequence Generation
por: Kiruluta, Andrew, et al.
Publicado: (2025)

Beyond Self Attention: A Subquadratic Fourier Wavelet Transformer with Multi Modal Fusion
por: Kiruluta, Andrew, et al.
Publicado: (2021)

MM-PoE: Multiple Choice Reasoning via. Process of Elimination using Multi-Modal Models
por: Chakrabarty, Sayak, et al.
Publicado: (2024)

Self-Correcting Large Language Models: Generation vs. Multiple Choice
por: Rahmani, Hossein A., et al.
Publicado: (2025)

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions
por: Park, Yoonah, et al.
Publicado: (2025)

Pushing the Boundaries of Multiple Choice Evaluation to One Hundred Options
por: Lee, Nahyun, et al.
Publicado: (2026)

Reason to Rote: Rethinking Memorization in Reasoning
por: Du, Yupei, et al.
Publicado: (2025)

Improving Score Reliability of Multiple Choice Benchmarks with Consistency Evaluation and Altered Answer Choices
por: Cavalin, Paulo, et al.
Publicado: (2025)