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 2751–2775 of 10817 papers

TitleStatusHype
Mitigating Low-Level Visual Hallucinations Requires Self-Awareness: Database, Model and Training Strategy—0
Instruction-Oriented Preference Alignment for Enhancing Multi-Modal Comprehension Capability of MLLMs—0
Vision-Amplified Semantic Entropy for Hallucination Detection in Medical Visual Question Answering—0
VGAT: A Cancer Survival Analysis Framework Transitioning from Generative Visual Question Answering to Genomic ReconstructionCode0
Improved Alignment of Modalities in Large Vision Language Models—0
Can Vision-Language Models Answer Face to Face Questions in the Real-World?—0
ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation—0
LEGO-Puzzles: How Good Are MLLMs at Multi-Step Spatial Reasoning?—0
BiblioPage: A Dataset of Scanned Title Pages for Bibliographic Metadata ExtractionCode0
DomainCQA: Crafting Expert-Level QA from Domain-Specific Charts—0
DeCAP: Context-Adaptive Prompt Generation for Debiasing Zero-shot Question Answering in Large Language Models—0
VectorFit : Adaptive Singular & Bias Vector Fine-Tuning of Pre-trained Foundation Models—0
KSHSeek: Data-Driven Approaches to Mitigating and Detecting Knowledge-Shortcut Hallucinations in Generative Models—0
Context-Efficient Retrieval with Factual Decomposition—0
ImF: Implicit Fingerprint for Large Language Models—0
Open-Vocabulary Functional 3D Scene Graphs for Real-World Indoor Spaces—0
Synthetic Function Demonstrations Improve Generation in Low-Resource Programming Languages—0
DiN: Diffusion Model for Robust Medical VQA with Semantic Noisy Labels—0
MAGIC-VQA: Multimodal And Grounded Inference with Commonsense Knowledge for Visual Question Answering—0
Where is this coming from? Making groundedness count in the evaluation of Document VQA models—0
When is dataset cartography ineffective? Using training dynamics does not improve robustness against Adversarial SQuAD—0
A Survey of Large Language Model Agents for Question Answering—0
Unmasking Deceptive Visuals: Benchmarking Multimodal Large Language Models on Misleading Chart Question Answering—0
SLIDE: Sliding Localized Information for Document Extraction—0
Expanding the Boundaries of Vision Prior Knowledge in Multi-modal Large Language Models—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