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 151–175 of 10817 papers

TitleStatusHype
Exploring the Impact of Occupational Personas on Domain-Specific QA—0
Grid-LOGAT: Grid Based Local and Global Area Transcription for Video Question Answering—0
A Simple Linear Patch Revives Layer-Pruned Large Language Models—0
Pangu DeepDiver: Adaptive Search Intensity Scaling via Open-Web Reinforcement Learning—0
Revisiting Epistemic Markers in Confidence Estimation: Can Markers Accurately Reflect Large Language Models' Uncertainty?Code0
Vision LLMs Are Bad at Hierarchical Visual Understanding, and LLMs Are the Bottleneck—0
LGAR: Zero-Shot LLM-Guided Neural Ranking for Abstract Screening in Systematic Literature ReviewsCode0
VideoCAD: A Large-Scale Video Dataset for Learning UI Interactions and 3D Reasoning from CAD SoftwareCode1
Improving Reliability and Explainability of Medical Question Answering through Atomic Fact Checking in Retrieval-Augmented LLMs—0
Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models—0
MedPAIR: Measuring Physicians and AI Relevance Alignment in Medical Question Answering—0
mRAG: Elucidating the Design Space of Multi-modal Retrieval-Augmented Generation—0
TCM-Ladder: A Benchmark for Multimodal Question Answering on Traditional Chinese Medicine—0
Reinforcement Learning for Better Verbalized Confidence in Long-Form Generation—0
From Chat Logs to Collective Insights: Aggregative Question Answering—0
VAU-R1: Advancing Video Anomaly Understanding via Reinforcement Fine-TuningCode2
Fortune: Formula-Driven Reinforcement Learning for Symbolic Table Reasoning in Language Models—0
Let's Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM's Math Capability—0
Differential Information: An Information-Theoretic Perspective on Preference Optimization—0
Puzzled by Puzzles: When Vision-Language Models Can't Take a HintCode1
VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC VideosCode0
Data-efficient Meta-models for Evaluation of Context-based Questions and Answers in LLMs—0
Diagnosing and Addressing Pitfalls in KG-RAG Datasets: Toward More Reliable Benchmarking—0
ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering—0
QLIP: A Dynamic Quadtree Vision Prior Enhances MLLM Performance Without RetrainingCode0
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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