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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
MedG-KRP: Medical Graph Knowledge Representation ProbingCode0
LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering0
KnowledgePrompts: Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced PromptingCode0
SandboxAQ's submission to MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval0
Addressing Blind Guessing: Calibration of Selection Bias in Multiple-Choice Question Answering by Video Language Models0
Differentiating Choices via Commonality for Multiple-Choice Question AnsweringCode0
Answer, Assemble, Ace: Understanding How Transformers Answer Multiple Choice Questions0
Long Story Short: Story-level Video Understanding from 20K Short Films0
EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential ReasoningCode0
AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation ExpertsCode1
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