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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 26–50 of 65 papers

TitleStatusHype
LLMs May Perform MCQA by Selecting the Least Incorrect Option—0
MEDITRON-70B: Scaling Medical Pretraining for Large Language ModelsCode4
Evaluating the Symbol Binding Ability of Large Language Models for Multiple-Choice Questions in Vietnamese General Education—0
Fool Your (Vision and) Language Model With Embarrassingly Simple PermutationsCode1
BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicineCode0
Llama 2: Open Foundation and Fine-Tuned Chat ModelsCode8
M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language ModelsCode1
Towards Expert-Level Medical Question Answering with Large Language ModelsCode1
FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domainCode0
BloombergGPT: A Large Language Model for FinanceCode0
Generating multiple-choice questions for medical question answering with distractors and cue-masking—0
Large Language Models Encode Clinical KnowledgeCode1
Galactica: A Large Language Model for ScienceCode4
Leveraging Large Language Models for Multiple Choice Question AnsweringCode1
Variational Open-Domain Question AnsweringCode1
Can large language models reason about medical questions?Code1
HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method—0
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 Answering—0
QuALITY: Question Answering with Long Input Texts, Yes!Code1
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