Cover Image

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Park, Yoonah, Pyun, Haesung, Jo, Yohan
Format:	Preprint
Published:	2025
Subjects:	Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Improving Dialogue State Tracking through Combinatorial Search for In-Context Examples
by: Pyun, Haesung, et al.
Published: (2025)

Don't Adapt Small Language Models for Tools; Adapt Tool Schemas to the Models
by: Lee, Jonggeun, et al.
Published: (2025)

Learning to Retrieve User History and Generate User Profiles for Personalized Persuasiveness Prediction
by: Park, Sejun, et al.
Published: (2026)

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

Mind the Gap: A Closer Look at Tokenization for Multiple-Choice Question Answering with LLMs
by: Sanz-Guerrero, Mario, et al.
Published: (2025)

Human Psychometric Questionnaires Mischaracterize LLM Behavior
by: Song, Woojung, et al.
Published: (2025)

SUIT: Knowledge Editing with Subspace-Aware Key-Value Mappings
by: Park, Haewon, et al.
Published: (2025)

Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?
by: Balepur, Nishant, et al.
Published: (2024)

Context-Robust Knowledge Editing for Language Models
by: Park, Haewon, et al.
Published: (2025)

Agentic Medical Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge
by: Rezaei, Mohammad Reza, et al.
Published: (2025)

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

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

Enhancing Clinical Multiple-Choice Questions Benchmarks with Knowledge Graph Guided Distractor Generation
by: Yang, Running, et al.
Published: (2025)

Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration
by: Yu, Han-Cheng, 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)

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

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

Bridging the Knowledge-Action Gap by Evaluating LLMs in Dynamic Dental Clinical Scenarios
by: Ma, Hongyang, et al.
Published: (2026)

Improving LLM First-Token Predictions in Multiple-Choice Question Answering via Output Prefilling
by: Cappelletti, Silvia, et al.
Published: (2025)

Thinking Like a Doctor: Conversational Diagnosis through the Exploration of Diagnostic Knowledge Graphs
by: Won, Jeongmoon, et al.
Published: (2026)

Generalizing Visual Question Answering from Synthetic to Human-Written Questions via a Chain of QA with a Large Language Model
by: Kim, Taehee, et al.
Published: (2024)

Correctness Coverage Evaluation for Medical Multiple-Choice Question Answering Based on the Enhanced Conformal Prediction Framework
by: Ke, Yusong, et al.
Published: (2025)

TriBench-Ko: Evaluating LLM Risks in Judicial Workflows
by: Lee, Haesung, et al.
Published: (2026)

Quantifying Data Contamination in Psychometric Evaluations of LLMs
by: Han, Jongwook, et al.
Published: (2025)

SpokenUS: A Spoken User Simulator for Task-Oriented Dialogue
by: Lee, Jonggeun, et al.
Published: (2026)

Question Difficulty Ranking for Multiple-Choice Reading Comprehension
by: Raina, Vatsal, et al.
Published: (2024)

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

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering
by: Sutanto, Patrick, et al.
Published: (2024)

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

Bridging the Preference Gap between Retrievers and LLMs
by: Ke, Zixuan, 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)

CUPID: Evaluating Personalized and Contextualized Alignment of LLMs from Interactions
by: Kim, Tae Soo, et al.
Published: (2025)

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

Polyglots or Multitudes? Multilingual LLM Answers to Value-laden Multiple-Choice Questions
by: Labat, Léo, et al.
Published: (2026)

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

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

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

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

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