Question Answering
2874 papers with code • 143 benchmarks • 360 datasets
Question Answering is the task of answering questions (typically reading comprehension questions), but abstaining when presented with a question that cannot be answered based on the provided context.
Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.
( Image credit: SQuAD )
Benchmarks
These leaderboards are used to track progress in Question Answering
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Libraries
Use these libraries to find Question Answering models and implementationsDatasets
MS COCO
MNIST
SQuAD
Natural Questions
MS MARCO
ConceptNet
TriviaQA
Reddit
HotpotQA
CNN/Daily Mail
Subtasks
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Open-Ended Question Answering
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Open-Domain Question Answering
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Conversational Question Answering
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Answer Selection
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Answer Selection
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Knowledge Base Question Answering
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Community Question Answering
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Zero-Shot Video Question Answer
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Multiple Choice Question Answering (MCQA)
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Long Form Question Answering
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Science Question Answering
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Generative Question Answering
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Cross-Lingual Question Answering
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Mathematical Question Answering
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Temporal/Casual QA
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Logical Reasoning Question Answering
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Multilingual Machine Comprehension in English Hindi
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True or False Question Answering
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Question Quality Assessment
Latest papers
EuSQuAD: Automatically Translated and Aligned SQuAD2.0 for Basque
The widespread availability of Question Answering (QA) datasets in English has greatly facilitated the advancement of the Natural Language Processing (NLP) field.
Consistency Training by Synthetic Question Generation for Conversational Question Answering
In our novel model-agnostic approach, referred to as CoTaH (Consistency-Trained augmented History), we augment the historical information with synthetic questions and subsequently employ consistency training to train a model that utilizes both real and augmented historical data to implicitly make the reasoning robust to irrelevant history.
ViTextVQA: A Large-Scale Visual Question Answering Dataset for Evaluating Vietnamese Text Comprehension in Images
Visual Question Answering (VQA) is a complicated task that requires the capability of simultaneously processing natural language and images.
Spiral of Silences: How is Large Language Model Killing Information Retrieval? -- A Case Study on Open Domain Question Answering
The practice of Retrieval-Augmented Generation (RAG), which integrates Large Language Models (LLMs) with retrieval systems, has become increasingly prevalent.
TextCoT: Zoom In for Enhanced Multimodal Text-Rich Image Understanding
The image overview stage provides a comprehensive understanding of the global scene information, and the coarse localization stage approximates the image area containing the answer based on the question asked.
Bridging Vision and Language Spaces with Assignment Prediction
park-jungin/vlap
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This paper introduces VLAP, a novel approach that bridges pretrained vision models and large language models (LLMs) to make frozen LLMs understand the visual world.
Constructing Benchmarks and Interventions for Combating Hallucinations in LLMs
technion-cs-nlp/hallucination-mitigation
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In this work, we first introduce an approach for constructing datasets based on the model knowledge for detection and intervention methods in closed-book and open-book question-answering settings.
CuriousLLM: Elevating Multi-Document QA with Reasoning-Infused Knowledge Graph Prompting
In the field of Question Answering (QA), unifying large language models (LLMs) with external databases has shown great success.
Synthetic Dataset Creation and Fine-Tuning of Transformer Models for Question Answering in Serbian
In this paper, we focus on generating a synthetic question answering (QA) dataset using an adapted Translate-Align-Retrieve method.
Enhancing Visual Question Answering through Question-Driven Image Captions as Prompts
This study explores the impact of incorporating image captioning as an intermediary process within the VQA pipeline.
