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 851875 of 10817 papers

TitleStatusHype
Does Vision-and-Language Pretraining Improve Lexical Grounding?Code1
Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World EnvironmentsCode1
DrBenchmark: A Large Language Understanding Evaluation Benchmark for French Biomedical DomainCode1
APOLLO: An Optimized Training Approach for Long-form Numerical ReasoningCode1
Fine-Grained Evaluation of Large Vision-Language Models in Autonomous DrivingCode1
An Optimal Algorithm for Finding Champions in Tournament GraphsCode1
DocVXQA: Context-Aware Visual Explanations for Document Question AnsweringCode1
DocVQA: A Dataset for VQA on Document ImagesCode1
Does Time Have Its Place? Temporal Heads: Where Language Models Recall Time-specific InformationCode1
DREAM: Improving Situational QA by First Elaborating the SituationCode1
Ditch the Gold Standard: Re-evaluating Conversational Question AnsweringCode1
Distinguishing Ignorance from Error in LLM HallucinationsCode1
Diversify Question Generation with Retrieval-Augmented Style TransferCode1
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation ExtractionCode1
AI2-THOR: An Interactive 3D Environment for Visual AICode1
Distilled Dual-Encoder Model for Vision-Language UnderstandingCode1
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighterCode1
Distilling Knowledge from Reader to Retriever for Question AnsweringCode1
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and IntentsCode1
Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question AnsweringCode1
DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question AnsweringCode1
Distantly-Supervised Dense Retrieval Enables Open-Domain Question Answering without Evidence AnnotationCode1
Discovering Spatio-Temporal Rationales for Video Question AnsweringCode1
A Simple LLM Framework for Long-Range Video Question-AnsweringCode1
Disentangling 3D Prototypical Networks For Few-Shot Concept LearningCode1
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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