SOTAVerified

Question Answering

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 )

Papers

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Showing 8826–8850 of 10817 papers

TitleStatusHype
FVQA: Fact-based Visual Question Answering—0
Game-theoretic Vocabulary Selection via the Shapley Value and Banzhaf Index—0
GANDALF: a General Character Name Description Dataset for Long Fiction—0
Gated Group Self-Attention for Answer Selection—0
Gated Self-Matching Networks for Reading Comprehension and Question Answering—0
GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records—0
Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering—0
GC-KBVQA: A New Four-Stage Framework for Enhancing Knowledge Based Visual Question Answering Performance—0
GeAR: Graph-enhanced Agent for Retrieval-augmented Generation—0
GEMeX: A Large-Scale, Groundable, and Explainable Medical VQA Benchmark for Chest X-ray Diagnosis—0
GEMeX-ThinkVG: Towards Thinking with Visual Grounding in Medical VQA via Reinforcement Learning—0
Gemini Pro Defeated by GPT-4V: Evidence from Education—0
GeMQuAD : Generating Multilingual Question Answering Datasets from Large Language Models using Few Shot Learning—0
GEM-RAG: Graphical Eigen Memories For Retrieval Augmented Generation—0
GenDec: A robust generative Question-decomposition method for Multi-hop reasoning—0
Gender and Racial Bias in Visual Question Answering Datasets—0
Gender and Racial Stereotype Detection in Legal Opinion Word Embeddings—0
General Embedding vs. Task-Specific Embedding: A Comparative Approach to Enhancing NLP Performance—0
Generalizable Neuro-symbolic Systems for Commonsense Question Answering—0
Generalization Differences between End-to-End and Neuro-Symbolic Vision-Language Reasoning Systems—0
Generalization Methods for In-Domain and Cross-Domain Opinion Holder Extraction—0
Generalization v.s. Memorization: Tracing Language Models' Capabilities Back to Pretraining Data—0
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness—0
Generalized Hadamard-Product Fusion Operators for Visual Question Answering—0
Generalizing Question Answering System with Pre-trained Language Model Fine-tuning—0
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All datasetsSQuAD2.0SQuAD1.1HotpotQAPIQABoolQCOPATriviaQASQuAD1.1 devNatural QuestionsOpenBookQATruthfulQAMultiRC

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1IE-Net (ensemble)EM90.94—Unverified
2FPNet (ensemble)EM90.87—Unverified
3IE-NetV2 (ensemble)EM90.86—Unverified
4SA-Net on Albert (ensemble)EM90.72—Unverified
5SA-Net-V2 (ensemble)EM90.68—Unverified
6FPNet (ensemble)EM90.6—Unverified
7Retro-Reader (ensemble)EM90.58—Unverified
8EntitySpanFocusV2 (ensemble)EM90.52—Unverified
9TransNets + SFVerifier + SFEnsembler (ensemble)EM90.49—Unverified
10EntitySpanFocus+AT (ensemble)EM90.45—Unverified