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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 1–50 of 65 papers

TitleStatusHype
Llama 2: Open Foundation and Fine-Tuned Chat ModelsCode8
Training Compute-Optimal Large Language ModelsCode6
MEDITRON-70B: Scaling Medical Pretraining for Large Language ModelsCode4
Galactica: A Large Language Model for ScienceCode4
PaLM: Scaling Language Modeling with PathwaysCode2
Scaling Language Models: Methods, Analysis & Insights from Training GopherCode2
MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question AnsweringCode2
AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation ExpertsCode1
Can large language models reason about medical questions?Code1
Clues Before Answers: Generation-Enhanced Multiple-Choice QACode1
Counterfactual Variable Control for Robust and Interpretable Question AnsweringCode1
Fool Your (Vision and) Language Model With Embarrassingly Simple PermutationsCode1
IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian LanguagesCode1
Large Language Models Encode Clinical KnowledgeCode1
Leveraging Large Language Models for Multiple Choice Question AnsweringCode1
LexGLUE: A Benchmark Dataset for Legal Language Understanding in EnglishCode1
M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language ModelsCode1
QuALITY: Question Answering with Long Input Texts, Yes!Code1
Towards Expert-Level Medical Question Answering with Large Language ModelsCode1
Variational Open-Domain Question AnsweringCode1
What do we expect from Multiple-choice QA Systems?—0
Fine-tuning BERT with Focus Words for Explanation Regeneration—0
Rethinking Generative Large Language Model Evaluation for Semantic Comprehension—0
KorMedMCQA: Multi-Choice Question Answering Benchmark for Korean Healthcare Professional Licensing Examinations—0
SandboxAQ's submission to MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval—0
Answer, Assemble, Ace: Understanding How Transformers Answer Multiple Choice Questions—0
Long Story Short: Story-level Video Understanding from 20K Short Films—0
Context-guided Triple Matching for Multiple Choice Question Answering—0
Context-guided Triple Matching for Multiple Choice Question Answering—0
Context Modeling with Evidence Filter for Multiple Choice Question Answering—0
Correctness Coverage Evaluation for Medical Multiple-Choice Question Answering Based on the Enhanced Conformal Prediction Framework—0
CP-Router: An Uncertainty-Aware Router Between LLM and LRM—0
Transliteration: A Simple Technique For Improving Multilingual Language Modeling—0
Disaggregating Hops: Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at each Hop?—0
First Token Probability Guided RAG for Telecom Question Answering—0
Unsupervised multiple choices question answering via universal corpus—0
Evaluating the Symbol Binding Ability of Large Language Models for Multiple-Choice Questions in Vietnamese General Education—0
Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above—0
Generating multiple-choice questions for medical question answering with distractors and cue-masking—0
LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering—0
Visual7W: Grounded Question Answering in Images—0
Healthy LLMs? Benchmarking LLM Knowledge of UK Government Public Health Information—0
HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method—0
LLMs May Perform MCQA by Selecting the Least Incorrect Option—0
Med-RLVR: Emerging Medical Reasoning from a 3B base model via reinforcement Learning—0
Improving LLM First-Token Predictions in Multiple-Choice Question Answering via Prefilling Attack—0
Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering—0
Addressing Blind Guessing: Calibration of Selection Bias in Multiple-Choice Question Answering by Video Language Models—0
Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?Code0
FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domainCode0
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