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 9651–9700 of 10817 papers

TitleStatusHype
Visual Question Answering based on Formal Logic—0
Visual Question Answering based on Local-Scene-Aware Referring Expression Generation—0
Visual Question Answering Dataset for Bilingual Image Understanding: A Study of Cross-Lingual Transfer Using Attention Maps—0
Visual Question Answering for Cultural Heritage—0
Visual question answering: from early developments to recent advances -- a survey—0
Visual Question Answering in Ophthalmology: A Progressive and Practical Perspective—0
Visual Question Answering in Remote Sensing with Cross-Attention and Multimodal Information Bottleneck—0
Visual Question Answering Instruction: Unlocking Multimodal Large Language Model To Domain-Specific Visual Multitasks—0
Visual Question Answering in the Medical Domain—0
Visual Question Answering on 360° Images—0
Visual Question Answering on Image Sets—0
Visual Question Answering on Multiple Remote Sensing Image Modalities—0
Visual Question Answering Using Semantic Information from Image Descriptions—0
Visual Question Answering (VQA) on Images with Superimposed Text—0
Visual Question Answering with Memory-Augmented Networks—0
Visual Question Answering with Prior Class Semantics—0
Visual Question Answering with Question Representation Update (QRU)—0
Visual Question Generation as Dual Task of Visual Question Answering—0
Visual Question: Predicting If a Crowd Will Agree on the Answer—0
Visual Question Reasoning on General Dependency Tree—0
Visual Reference Resolution using Attention Memory for Visual Dialog—0
Visual Relationship Detection using Scene Graphs: A Survey—0
VisualSimpleQA: A Benchmark for Decoupled Evaluation of Large Vision-Language Models in Fact-Seeking Question Answering—0
Visual Superordinate Abstraction for Robust Concept Learning—0
Visual TTR - Modelling Visual Question Answering in Type Theory with Records—0
ViT3D Alignment of LLaMA3: 3D Medical Image Report Generation—0
VITED: Video Temporal Evidence Distillation—0
ViUniT: Visual Unit Tests for More Robust Visual Programming—0
VLAB: Enhancing Video Language Pre-training by Feature Adapting and Blending—0
VL-BEiT: Generative Vision-Language Pretraining—0
VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks—0
VLMAE: Vision-Language Masked Autoencoder—0
VLM-Assisted Continual learning for Visual Question Answering in Self-Driving—0
VLM Q-Learning: Aligning Vision-Language Models for Interactive Decision-Making—0
VLMT: Vision-Language Multimodal Transformer for Multimodal Multi-hop Question Answering—0
VLR-Bench: Multilingual Benchmark Dataset for Vision-Language Retrieval Augmented Generation—0
VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension—0
EVJVQA Challenge: Multilingual Visual Question Answering—0
VolDoGer: LLM-assisted Datasets for Domain Generalization in Vision-Language Tasks—0
Voltron: A Hybrid System For Answer Validation Based On Lexical And Distance Features—0
VoxRAG: A Step Toward Transcription-Free RAG Systems in Spoken Question Answering—0
VQA-Aid: Visual Question Answering for Post-Disaster Damage Assessment and Analysis—0
VQABQ: Visual Question Answering by Basic Questions—0
VQA-Diff: Exploiting VQA and Diffusion for Zero-Shot Image-to-3D Vehicle Asset Generation in Autonomous Driving—0
VQA-E: Explaining, Elaborating, and Enhancing Your Answers for Visual Questions—0
VQA-GEN: A Visual Question Answering Benchmark for Domain Generalization—0
VQA-GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answering—0
VQA-LOL: Visual Question Answering under the Lens of Logic—0
VQA-MHUG: A Gaze Dataset to Study Multimodal Neural Attention in Visual Question Answering—0
VQA Training Sets are Self-play Environments for Generating Few-shot Pools—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