Multiple-choice
231 papers with code • 1 benchmarks • 7 datasets
Libraries
Use these libraries to find Multiple-choice models and implementationsMost implemented papers
VQA: Visual Question Answering
Given an image and a natural language question about the image, the task is to provide an accurate natural language answer.
Flamingo: a Visual Language Model for Few-Shot Learning
Building models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research.
From Recognition to Cognition: Visual Commonsense Reasoning
While this task is easy for humans, it is tremendously difficult for today's vision systems, requiring higher-order cognition and commonsense reasoning about the world.
Revisiting Visual Question Answering Baselines
Visual question answering (VQA) is an interesting learning setting for evaluating the abilities and shortcomings of current systems for image understanding.
Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering
In this paper we propose a retriever-reader model that learns to attend on essential terms during the question answering process.
CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
To investigate question answering with prior knowledge, we present CommonsenseQA: a challenging new dataset for commonsense question answering.
What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams
Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community.
GPT Takes the Bar Exam
Nearly all jurisdictions in the United States require a professional license exam, commonly referred to as "the Bar Exam," as a precondition for law practice.
Steering Llama 2 via Contrastive Activation Addition
We introduce Contrastive Activation Addition (CAA), an innovative method for steering language models by modifying their activations during forward passes.
Confident Multiple Choice Learning
Ensemble methods are arguably the most trustworthy techniques for boosting the performance of machine learning models.