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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 41–50 of 65 papers

TitleStatusHype
Visual7W: Grounded Question Answering in Images—0
Healthy LLMs? Benchmarking LLM Knowledge of UK Government Public Health Information—0
HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method—0
LLMs May Perform MCQA by Selecting the Least Incorrect Option—0
Med-RLVR: Emerging Medical Reasoning from a 3B base model via reinforcement Learning—0
Improving LLM First-Token Predictions in Multiple-Choice Question Answering via Prefilling Attack—0
Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering—0
Addressing Blind Guessing: Calibration of Selection Bias in Multiple-Choice Question Answering by Video Language Models—0
Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?Code0
FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domainCode0
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