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 6201–6250 of 10817 papers

TitleStatusHype
MED: The LMU System for the SIGMORPHON 2016 Shared Task on Morphological Reinflection—0
MedThink: Explaining Medical Visual Question Answering via Multimodal Decision-Making Rationale—0
Modality-Aware Integration with Large Language Models for Knowledge-based Visual Question Answering—0
MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning—0
Model Agnostic Answer Reranking System for Adversarial Question Answering—0
MedXChat: A Unified Multimodal Large Language Model Framework towards CXRs Understanding and Generation—0
Model Compression with Two-stage Multi-teacher Knowledge Distillation for Web Question Answering System—0
Continuous Training and Fine-tuning for Domain-Specific Language Models in Medical Question Answering—0
MegaWika: Millions of reports and their sources across 50 diverse languages—0
MEGC2025: Micro-Expression Grand Challenge on Spot Then Recognize and Visual Question Answering—0
How well do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation—0
MEKER: Memory Efficient Knowledge Embedding Representation for Link Prediction and Question Answering—0
Effective shared representations with Multitask Learning for Community Question Answering—0
A Survey on Table-and-Text HybridQA: Concepts, Methods, Challenges and Future Directions—0
How Well can We Learn Interpretable Entity Types from Text?—0
How Well Can Vison-Language Models Understand Humans' Intention? An Open-ended Theory of Mind Question Evaluation Benchmark—0
MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension—0
Memento No More: Coaching AI Agents to Master Multiple Tasks via Hints Internalization—0
MNLP at MEDIQA 2021: Fine-Tuning PEGASUS for Consumer Health Question Summarization—0
Memento: Note-Taking for Your Future Self—0
MemInsight: Autonomous Memory Augmentation for LLM Agents—0
MEMOIR: Lifelong Model Editing with Minimal Overwrite and Informed Retention for LLMs—0
How Vision-Language Tasks Benefit from Large Pre-trained Models: A Survey—0
How Transferable are Reasoning Patterns in VQA?—0
Memory-Augmented Multimodal LLMs for Surgical VQA via Self-Contained Inquiry—0
Memory Augmented Neural Networks for Natural Language Processing—0
Memory Augmented Sequential Paragraph Retrieval for Multi-hop Question Answering—0
Memory-Aware and Uncertainty-Guided Retrieval for Multi-Hop Question Answering—0
Memory-Based Acquisition of Argument Structures and its Application to Implicit Role Detection—0
Continuous Perception Benchmark—0
A Survey on State-of-the-art Techniques for Knowledge Graphs Construction and Challenges ahead—0
Memory-Centric Embodied Question Answer—0
Efficient and Interpretable Neural Models for Entity Tracking—0
Memory Graph Networks for Explainable Memory-grounded Question Answering—0
A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input—0
How to Seq2seq for SQL—0
How to Pre-Train Your Model? Comparison of Different Pre-Training Models for Biomedical Question Answering—0
Continuous Active Learning Using Pretrained Transformers—0
How to Mitigate Information Loss in Knowledge Graphs for GraphRAG: Leveraging Triple Context Restoration and Query-Driven Feedback—0
How to make qubits speak—0
MentalQA: An Annotated Arabic Corpus for Questions and Answers of Mental Healthcare—0
Efficient Context Selection for Long-Context QA: No Tuning, No Iteration, Just Adaptive-k—0
A Survey on Semantic Parsing from the perspective of Compositionality—0
Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs—0
How to Make a BLT Sandwich? Learning to Reason towards Understanding Web Instructional Videos—0
Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities—0
How to find a good image-text embedding for remote sensing visual question answering?—0
Meta Answering for Machine Reading—0
How to Evaluate Opinionated Keyphrase Extraction?—0
Continually Self-Improving Language Models for Bariatric Surgery Question--Answering—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