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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

Showing 1120 of 65 papers

TitleStatusHype
Towards Expert-Level Medical Question Answering with Large Language ModelsCode1
Large Language Models Encode Clinical KnowledgeCode1
Leveraging Large Language Models for Multiple Choice Question AnsweringCode1
Variational Open-Domain Question AnsweringCode1
Can large language models reason about medical questions?Code1
Clues Before Answers: Generation-Enhanced Multiple-Choice QACode1
QuALITY: Question Answering with Long Input Texts, Yes!Code1
LexGLUE: A Benchmark Dataset for Legal Language Understanding in EnglishCode1
IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian LanguagesCode1
Counterfactual Variable Control for Robust and Interpretable Question AnsweringCode1
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