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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 51–65 of 65 papers

TitleStatusHype
First Token Probability Guided RAG for Telecom Question Answering—0
Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above—0
Evaluating the Symbol Binding Ability of Large Language Models for Multiple-Choice Questions in Vietnamese General Education—0
Rethinking Generative Large Language Model Evaluation for Semantic Comprehension—0
Disaggregating Hops: Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at each Hop?—0
Generating multiple-choice questions for medical question answering with distractors and cue-masking—0
Healthy LLMs? Benchmarking LLM Knowledge of UK Government Public Health Information—0
HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method—0
Improving LLM First-Token Predictions in Multiple-Choice Question Answering via Prefilling Attack—0
Addressing Blind Guessing: Calibration of Selection Bias in Multiple-Choice Question Answering by Video Language Models—0
SandboxAQ's submission to MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval—0
KorMedMCQA: Multi-Choice Question Answering Benchmark for Korean Healthcare Professional Licensing Examinations—0
Transliteration: A Simple Technique For Improving Multilingual Language Modeling—0
Answer, Assemble, Ace: Understanding How Transformers Answer Multiple Choice Questions—0
Unsupervised multiple choices question answering via universal corpus—0
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