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Multiple Choice Question Answering (MCQA)
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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.
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Improving LLM First-Token Predictions in Multiple-Choice Question Answering via Prefilling Attack
Healthy LLMs? Benchmarking LLM Knowledge of UK Government Public Health Information
Question-Aware Knowledge Graph Prompting for Enhancing Large Language Models
Correctness Coverage Evaluation for Medical Multiple-Choice Question Answering Based on the Enhanced Conformal Prediction Framework
Med-RLVR: Emerging Medical Reasoning from a 3B base model via reinforcement Learning
Wrong Answers Can Also Be Useful: PlausibleQA -- A Large-Scale QA Dataset with Answer Plausibility Scores
Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above
Investigating the Shortcomings of LLMs in Step-by-Step Legal Reasoning
First Token Probability Guided RAG for Telecom Question Answering
MedG-KRP: Medical Graph Knowledge Representation Probing
LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering
KnowledgePrompts: Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting
SandboxAQ's submission to MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval
Addressing Blind Guessing: Calibration of Selection Bias in Multiple-Choice Question Answering by Video Language Models
Differentiating Choices via Commonality for Multiple-Choice Question Answering
Answer, Assemble, Ace: Understanding How Transformers Answer Multiple Choice Questions
Long Story Short: Story-level Video Understanding from 20K Short Films
EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning
AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts
From Multiple-Choice to Extractive QA: A Case Study for English and Arabic
Rethinking Generative Large Language Model Evaluation for Semantic Comprehension
KorMedMCQA: Multi-Choice Question Answering Benchmark for Korean Healthcare Professional Licensing Examinations
Unsupervised multiple choices question answering via universal corpus
Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?
LLMs May Perform MCQA by Selecting the Least Incorrect Option
MEDITRON-70B: Scaling Medical Pretraining for Large Language Models
Evaluating the Symbol Binding Ability of Large Language Models for Multiple-Choice Questions in Vietnamese General Education
Fool Your (Vision and) Language Model With Embarrassingly Simple Permutations
BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicine
Llama 2: Open Foundation and Fine-Tuned Chat Models
M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models
PaLM 2 Technical Report
Towards Expert-Level Medical Question Answering with Large Language Models
FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain
BloombergGPT: A Large Language Model for Finance
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