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 2126–2150 of 10817 papers

TitleStatusHype
Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over WikidataCode1
AmbigQA: Answering Ambiguous Open-domain QuestionsCode1
Eliminating Position Bias of Language Models: A Mechanistic ApproachCode1
Complex Knowledge Base Question Answering: A SurveyCode1
Development and bilingual evaluation of Japanese medical large language model within reasonably low computational resourcesCode1
A Memory Efficient Baseline for Open Domain Question AnsweringCode1
Empower Entity Set Expansion via Language Model ProbingCode1
DegreEmbed: incorporating entity embedding into logic rule learning for knowledge graph reasoningCode1
Complex Reasoning over Logical Queries on Commonsense Knowledge GraphsCode1
DeFormer: Decomposing Pre-trained Transformers for Faster Question AnsweringCode1
Complex Temporal Question Answering on Knowledge GraphsCode1
Delaying Interaction Layers in Transformer-based Encoders for Efficient Open Domain Question AnsweringCode1
A Dataset for Medical Instructional Video Classification and Question AnsweringCode1
End-to-End Training of Neural Retrievers for Open-Domain Question AnsweringCode1
Engineering flexible machine learning systems by traversing functionally-invariant pathsCode1
Enhancing Complex Question Answering over Knowledge Graphs through Evidence Pattern RetrievalCode1
Enhancing Contextual Understanding in Large Language Models through Contrastive DecodingCode1
ECONET: Effective Continual Pretraining of Language Models for Event Temporal ReasoningCode1
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data ProcessingCode1
Enhancing Visual Question Answering through Question-Driven Image Captions as PromptsCode1
Entailment as Few-Shot LearnerCode1
Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven OptimizationCode1
Compositional Exemplars for In-context LearningCode1
EntQA: Entity Linking as Question AnsweringCode1
DELIFT: Data Efficient Language model Instruction Fine TuningCode1
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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