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 601–650 of 10817 papers

TitleStatusHype
RefChartQA: Grounding Visual Answer on Chart Images through Instruction TuningCode1
Can DeepSeek Reason Like a Surgeon? An Empirical Evaluation for Vision-Language Understanding in Robotic-Assisted Surgery—0
Patience is all you need! An agentic system for performing scientific literature review—0
How Well Can Vison-Language Models Understand Humans' Intention? An Open-ended Theory of Mind Question Evaluation Benchmark—0
Preference-based Learning with Retrieval Augmented Generation for Conversational Question AnsweringCode0
EgoToM: Benchmarking Theory of Mind Reasoning from Egocentric VideosCode1
AssistPDA: An Online Video Surveillance Assistant for Video Anomaly Prediction, Detection, and Analysis—0
MemInsight: Autonomous Memory Augmentation for LLM Agents—0
JEEM: Vision-Language Understanding in Four Arabic Dialects—0
CTRL-O: Language-Controllable Object-Centric Visual Representation Learning—0
ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented GenerationCode1
SWI: Speaking with Intent in Large Language ModelsCode0
Leveraging LLMs with Iterative Loop Structure for Enhanced Social Intelligence in Video Question Answering—0
AskSport: Web Application for Sports Question-Answering—0
Fine-Grained Evaluation of Large Vision-Language Models in Autonomous DrivingCode1
FaceBench: A Multi-View Multi-Level Facial Attribute VQA Dataset for Benchmarking Face Perception MLLMsCode1
A Survey of Multimodal Retrieval-Augmented Generation—0
Feature4X: Bridging Any Monocular Video to 4D Agentic AI with Versatile Gaussian Feature Fields—0
Vision-Amplified Semantic Entropy for Hallucination Detection in Medical Visual Question Answering—0
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
Unified Multimodal Discrete DiffusionCode2
Self-ReS: Self-Reflection in Large Vision-Language Models for Long Video Understanding—0
KSHSeek: Data-Driven Approaches to Mitigating and Detecting Knowledge-Shortcut Hallucinations in Generative Models—0
VectorFit : Adaptive Singular & Bias Vector Fine-Tuning of Pre-trained Foundation Models—0
Context-Efficient Retrieval with Factual Decomposition—0
DomainCQA: Crafting Expert-Level QA from Domain-Specific Charts—0
ImF: Implicit Fingerprint for Large Language Models—0
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
Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language ModelsCode1
VGAT: A Cancer Survival Analysis Framework Transitioning from Generative Visual Question Answering to Genomic ReconstructionCode0
BiblioPage: A Dataset of Scanned Title Pages for Bibliographic Metadata ExtractionCode0
LEGO-Puzzles: How Good Are MLLMs at Multi-Step Spatial Reasoning?—0
PAVE: Patching and Adapting Video Large Language ModelsCode1
Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image AnalysisCode2
DeCAP: Context-Adaptive Prompt Generation for Debiasing Zero-shot Question Answering in Large Language Models—0
Open-Vocabulary Functional 3D Scene Graphs for Real-World Indoor Spaces—0
Where is this coming from? Making groundedness count in the evaluation of Document VQA models—0
A Survey of Large Language Model Agents for Question Answering—0
When is dataset cartography ineffective? Using training dynamics does not improve robustness against Adversarial SQuAD—0
MC-LLaVA: Multi-Concept Personalized Vision-Language ModelCode2
MAGIC-VQA: Multimodal And Grounded Inference with Commonsense Knowledge for Visual Question Answering—0
DiN: Diffusion Model for Robust Medical VQA with Semantic Noisy Labels—0
Synthetic Function Demonstrations Improve Generation in Low-Resource Programming Languages—0
LLaVAction: evaluating and training multi-modal large language models for action recognitionCode2
Expanding the Boundaries of Vision Prior Knowledge in Multi-modal Large Language Models—0
SLIDE: Sliding Localized Information for Document Extraction—0
Retrieval Augmented Generation and Understanding in Vision: A Survey and New OutlookCode3
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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