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Multiple Choice Question Answering (MCQA)

A multiple-choice question (MCQ) is composed of two parts: a stem that identifies the question or problem, and a set of alternatives or possible answers that contain a key that is the best answer to the question, and a number of distractors that are plausible but incorrect answers to the question.

In a k-way MCQA task, a model is provided with a question q, a set of candidate options O = {O1, . . . , Ok}, and a supporting context for each option C = {C1, . . . , Ck}. The model needs to predict the correct answer option that is best supported by the given contexts.

Papers

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Showing 26–50 of 65 papers

TitleStatusHype
Med-RLVR: Emerging Medical Reasoning from a 3B base model via reinforcement Learning—0
Wrong Answers Can Also Be Useful: PlausibleQA -- A Large-Scale QA Dataset with Answer Plausibility ScoresCode0
Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above—0
Investigating the Shortcomings of LLMs in Step-by-Step Legal ReasoningCode0
First Token Probability Guided RAG for Telecom Question Answering—0
MedG-KRP: Medical Graph Knowledge Representation ProbingCode0
LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering—0
KnowledgePrompts: Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced PromptingCode0
SandboxAQ's submission to MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval—0
Addressing Blind Guessing: Calibration of Selection Bias in Multiple-Choice Question Answering by Video Language Models—0
Differentiating Choices via Commonality for Multiple-Choice Question AnsweringCode0
Answer, Assemble, Ace: Understanding How Transformers Answer Multiple Choice Questions—0
Long Story Short: Story-level Video Understanding from 20K Short Films—0
EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential ReasoningCode0
From Multiple-Choice to Extractive QA: A Case Study for English and ArabicCode0
Rethinking Generative Large Language Model Evaluation for Semantic Comprehension—0
KorMedMCQA: Multi-Choice Question Answering Benchmark for Korean Healthcare Professional Licensing Examinations—0
Unsupervised multiple choices question answering via universal corpus—0
Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?Code0
LLMs May Perform MCQA by Selecting the Least Incorrect Option—0
Evaluating the Symbol Binding Ability of Large Language Models for Multiple-Choice Questions in Vietnamese General Education—0
BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicineCode0
FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domainCode0
BloombergGPT: A Large Language Model for FinanceCode0
Generating multiple-choice questions for medical question answering with distractors and cue-masking—0
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