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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 5165 of 65 papers

TitleStatusHype
Rethinking Generative Large Language Model Evaluation for Semantic Comprehension0
Fine-tuning BERT with Focus Words for Explanation Regeneration0
First Token Probability Guided RAG for Telecom Question Answering0
Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above0
Disaggregating Hops: Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at each Hop?0
SandboxAQ's submission to MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval0
Answer, Assemble, Ace: Understanding How Transformers Answer Multiple Choice Questions0
Generating multiple-choice questions for medical question answering with distractors and cue-masking0
Healthy LLMs? Benchmarking LLM Knowledge of UK Government Public Health Information0
HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method0
Improving LLM First-Token Predictions in Multiple-Choice Question Answering via Prefilling Attack0
Addressing Blind Guessing: Calibration of Selection Bias in Multiple-Choice Question Answering by Video Language Models0
Long Story Short: Story-level Video Understanding from 20K Short Films0
KorMedMCQA: Multi-Choice Question Answering Benchmark for Korean Healthcare Professional Licensing Examinations0
Transliteration: A Simple Technique For Improving Multilingual Language Modeling0
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