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

Showing 20762100 of 10817 papers

TitleStatusHype
DSPNet: Dual-vision Scene Perception for Robust 3D Question AnsweringCode1
Dense Passage Retrieval for Open-Domain Question AnsweringCode1
Densely Connected Attention Propagation for Reading ComprehensionCode1
Describe Anything Model for Visual Question Answering on Text-rich ImagesCode1
Dense-Caption Matching and Frame-Selection Gating for Temporal Localization in VideoQACode1
DeVLBert: Learning Deconfounded Visio-Linguistic RepresentationsCode1
DEXTER: A Benchmark for open-domain Complex Question Answering using LLMsCode1
Dense and Aligned Captions (DAC) Promote Compositional Reasoning in VL ModelsCode1
Dense Hierarchical Retrieval for Open-Domain Question AnsweringCode1
Differentiable Reasoning on Large Knowledge Bases and Natural LanguageCode1
Designing a Minimal Retrieve-and-Read System for Open-Domain Question AnsweringCode1
Delaying Interaction Layers in Transformer-based Encoders for Efficient Open Domain Question AnsweringCode1
DegreEmbed: incorporating entity embedding into logic rule learning for knowledge graph reasoningCode1
Discovering Spatio-Temporal Rationales for Video Question AnsweringCode1
Disentangling 3D Prototypical Networks For Few-Shot Concept LearningCode1
Distantly-Supervised Dense Retrieval Enables Open-Domain Question Answering without Evidence AnnotationCode1
Combo of Thinking and Observing for Outside-Knowledge VQACode1
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighterCode1
Distilled Dual-Encoder Model for Vision-Language UnderstandingCode1
M3-Jepa: Multimodal Alignment via Multi-directional MoE based on the JEPA frameworkCode1
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and IntentsCode1
DELIFT: Data Efficient Language model Instruction Fine TuningCode1
Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven OptimizationCode1
A Long Way to Go: Investigating Length Correlations in RLHFCode1
DeFormer: Decomposing Pre-trained Transformers for Faster Question AnsweringCode1
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Benchmark Results

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