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 8801–8825 of 10817 papers

TitleStatusHype
Less Is More: Linear Layers on CLIP Features as Powerful VizWiz Model—0
Let Me Know What to Ask: Interrogative-Word-Aware Question Generation—0
Let Sense Bags Do Talking: Cross Lingual Word Semantic Similarity for English and Hindi—0
Let's Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM's Math Capability—0
Let's ViCE! Mimicking Human Cognitive Behavior in Image Generation Evaluation—0
Leveraging Chain of Thought towards Empathetic Spoken Dialogue without Corresponding Question-Answering Data—0
Leveraging Commonsense Knowledge on Classifying False News and Determining Checkworthiness of Claims—0
Leveraging Crowdsourcing for Paraphrase Recognition—0
Leveraging Entanglement Entropy for Deep Understanding of Attention Matrix in Text Matching—0
Leveraging Extracted Model Adversaries for Improved Black Box Attacks—0
Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering—0
Leveraging Graph Retrieval-Augmented Generation to Support Learners' Understanding of Knowledge Concepts in MOOCs—0
Leveraging Linguistic Structure For Open Domain Information Extraction—0
Leveraging LLMs with Iterative Loop Structure for Enhanced Social Intelligence in Video Question Answering—0
Leveraging Logical Rules in Knowledge Editing: A Cherry on the Top—0
Leveraging Medical Visual Question Answering with Supporting Facts—0
Leveraging Personal Navigation Assistant Systems Using Automated Social Media Traffic Reporting—0
Leveraging Pre-trained Models for Failure Analysis Triplets Generation—0
Leveraging Retrieval-Augmented Generation for Persian University Knowledge Retrieval—0
Leveraging Static Relationships for Intra-Type and Inter-Type Message Passing in Video Question Answering—0
Leveraging Structured Metadata for Improving Question Answering on the Web—0
Leveraging Synthetic Discourse Data via Multi-task Learning for Implicit Discourse Relation Recognition—0
Leveraging Term Banks for Answering Complex Questions: A Case for Sparse Vectors—0
Leveraging Textual Features for Best Answer Prediction in Community-based Question Answering—0
Leveraging the Domain Adaptation of Retrieval Augmented Generation Models for Question Answering and Reducing Hallucination—0
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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