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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 4150 of 65 papers

TitleStatusHype
Can large language models reason about medical questions?Code1
HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method0
Clues Before Answers: Generation-Enhanced Multiple-Choice QACode1
PaLM: Scaling Language Modeling with PathwaysCode2
Training Compute-Optimal Large Language ModelsCode6
MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question AnsweringCode2
Does Transliteration Help Multilingual Language Modeling?Code0
Disaggregating Hops: Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at each Hop?0
Context-guided Triple Matching for Multiple Choice Question Answering0
QuALITY: Question Answering with Long Input Texts, Yes!Code1
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