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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 31–40 of 65 papers

TitleStatusHype
Fine-tuning BERT with Focus Words for Explanation Regeneration—0
Disaggregating Hops: Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at each Hop?—0
KorMedMCQA: Multi-Choice Question Answering Benchmark for Korean Healthcare Professional Licensing Examinations—0
Transliteration: A Simple Technique For Improving Multilingual Language Modeling—0
Evaluating the Symbol Binding Ability of Large Language Models for Multiple-Choice Questions in Vietnamese General Education—0
First Token Probability Guided RAG for Telecom Question Answering—0
Unsupervised multiple choices question answering via universal corpus—0
Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above—0
BloombergGPT: A Large Language Model for Finance—0
LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering—0
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