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 251–275 of 10817 papers

TitleStatusHype
PPT: A Process-based Preference Learning Framework for Self Improving Table Question Answering Models—0
CXReasonBench: A Benchmark for Evaluating Structured Diagnostic Reasoning in Chest X-raysCode0
Deep Video Discovery: Agentic Search with Tool Use for Long-form Video Understanding—0
FinRAGBench-V: A Benchmark for Multimodal RAG with Visual Citation in the Financial Domain—0
QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement LearningCode4
VEAttack: Downstream-agnostic Vision Encoder Attack against Large Vision Language ModelsCode1
PerMedCQA: Benchmarking Large Language Models on Medical Consumer Question Answering in Persian Language—0
DanmakuTPPBench: A Multi-modal Benchmark for Temporal Point Process Modeling and UnderstandingCode2
MetaGen Blended RAG: Higher Accuracy for Domain-Specific Q&A Without Fine-TuningCode1
Task Specific Pruning with LLM-Sieve: How Many Parameters Does Your Task Really Need?—0
Analyzing Mitigation Strategies for Catastrophic Forgetting in End-to-End Training of Spoken Language Models—0
EarthSE: A Benchmark Evaluating Earth Scientific Exploration Capability for Large Language Models—0
VoxRAG: A Step Toward Transcription-Free RAG Systems in Spoken Question Answering—0
Augmenting LLM Reasoning with Dynamic Notes Writing for Complex QA—0
Are the Hidden States Hiding Something? Testing the Limits of Factuality-Encoding Capabilities in LLMs—0
Continually Self-Improving Language Models for Bariatric Surgery Question--Answering—0
Benchmarking Retrieval-Augmented Multimomal Generation for Document Question AnsweringCode1
Mitigating Hallucinations in Vision-Language Models through Image-Guided Head SuppressionCode1
Steering LVLMs via Sparse Autoencoder for Hallucination Mitigation—0
Teaching Large Language Models to Maintain Contextual Faithfulness via Synthetic Tasks and Reinforcement LearningCode1
CUB: Benchmarking Context Utilisation Techniques for Language Models—0
UNCLE: Uncertainty Expressions in Long-Form Generation—0
CT-Agent: A Multimodal-LLM Agent for 3D CT Radiology Question Answering—0
Collaboration among Multiple Large Language Models for Medical Question Answering—0
Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding—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