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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 11–20 of 65 papers

TitleStatusHype
M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language ModelsCode1
Large Language Models Encode Clinical KnowledgeCode1
Counterfactual Variable Control for Robust and Interpretable Question AnsweringCode1
Fool Your (Vision and) Language Model With Embarrassingly Simple PermutationsCode1
AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation ExpertsCode1
Leveraging Large Language Models for Multiple Choice Question AnsweringCode1
Can large language models reason about medical questions?Code1
IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian LanguagesCode1
Clues Before Answers: Generation-Enhanced Multiple-Choice QACode1
Variational Open-Domain Question AnsweringCode1
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