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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 21–30 of 65 papers

TitleStatusHype
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
MEDITRON-70B: Scaling Medical Pretraining for Large Language ModelsCode4
Evaluating the Symbol Binding Ability of Large Language Models for Multiple-Choice Questions in Vietnamese General Education—0
Fool Your (Vision and) Language Model With Embarrassingly Simple PermutationsCode1
BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicineCode0
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