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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
CP-Router: An Uncertainty-Aware Router Between LLM and LRM—0
Generating multiple-choice questions for medical question answering with distractors and cue-masking—0
LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering—0
Context Modeling with Evidence Filter for Multiple Choice Question Answering—0
Context-guided Triple Matching for Multiple Choice Question Answering—0
Answer, Assemble, Ace: Understanding How Transformers Answer Multiple Choice Questions—0
First Token Probability Guided RAG for Telecom Question Answering—0
Healthy LLMs? Benchmarking LLM Knowledge of UK Government Public Health Information—0
HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method—0
Fine-tuning BERT with Focus Words for Explanation Regeneration—0
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