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 10011025 of 10817 papers

TitleStatusHype
Ditch the Gold Standard: Re-evaluating Conversational Question AnsweringCode1
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and IntentsCode1
Attributed Question Answering: Evaluation and Modeling for Attributed Large Language ModelsCode1
KnowTuning: Knowledge-aware Fine-tuning for Large Language ModelsCode1
A Simple LLM Framework for Long-Range Video Question-AnsweringCode1
Korean-Specific Dataset for Table Question AnsweringCode1
Distilling Knowledge from Reader to Retriever for Question AnsweringCode1
AuditWen:An Open-Source Large Language Model for AuditCode1
At Which Training Stage Does Code Data Help LLMs Reasoning?Code1
Distilled Dual-Encoder Model for Vision-Language UnderstandingCode1
Distinguishing Ignorance from Error in LLM HallucinationsCode1
DocNLI: A Large-scale Dataset for Document-level Natural Language InferenceCode1
AllenAct: A Framework for Embodied AI ResearchCode1
Asking Clarification Questions to Handle Ambiguity in Open-Domain QACode1
Distantly-Supervised Dense Retrieval Enables Open-Domain Question Answering without Evidence AnnotationCode1
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation ExtractionCode1
Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question AnsweringCode1
Distantly-Supervised Evidence Retrieval Enables Question Answering without Evidence AnnotationCode1
Language Models as Science TutorsCode1
Language Models Learn to Mislead Humans via RLHFCode1
Disentangling 3D Prototypical Networks For Few-Shot Concept LearningCode1
Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQACode1
A Few More Examples May Be Worth Billions of ParametersCode1
DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question AnsweringCode1
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighterCode1
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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