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 20512075 of 10817 papers

TitleStatusHype
DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question AnsweringCode1
Differentiable Reasoning on Large Knowledge Bases and Natural LanguageCode1
Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge GraphsCode1
DialSim: A Real-Time Simulator for Evaluating Long-Term Multi-Party Dialogue Understanding of Conversational AgentsCode1
M3-Jepa: Multimodal Alignment via Multi-directional MoE based on the JEPA frameworkCode1
Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning StrategiesCode1
Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question AnsweringCode1
DEXTER: A Benchmark for open-domain Complex Question Answering using LLMsCode1
DeVLBert: Learning Deconfounded Visio-Linguistic RepresentationsCode1
Dialog Inpainting: Turning Documents into DialogsCode1
Detecting Hate Speech in Multi-modal MemesCode1
Development and bilingual evaluation of Japanese medical large language model within reasonably low computational resourcesCode1
DialogLM: Pre-trained Model for Long Dialogue Understanding and SummarizationCode1
Designing a Minimal Retrieve-and-Read System for Open-Domain Question AnsweringCode1
Describe Anything Model for Visual Question Answering on Text-rich ImagesCode1
Densely Connected Attention Propagation for Reading ComprehensionCode1
Dense Passage Retrieval for Open-Domain Question AnsweringCode1
Detecting and Preventing Hallucinations in Large Vision Language ModelsCode1
Distantly-Supervised Dense Retrieval Enables Open-Domain Question Answering without Evidence AnnotationCode1
DELIFT: Data Efficient Language model Instruction Fine TuningCode1
Delaying Interaction Layers in Transformer-based Encoders for Efficient Open Domain Question AnsweringCode1
Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven OptimizationCode1
ECONET: Effective Continual Pretraining of Language Models for Event Temporal ReasoningCode1
DeFormer: Decomposing Pre-trained Transformers for Faster Question AnsweringCode1
A Long Way to Go: Investigating Length Correlations in RLHFCode1
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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