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 551–600 of 10817 papers

TitleStatusHype
E.T. Bench: Towards Open-Ended Event-Level Video-Language UnderstandingCode2
ERA-CoT: Improving Chain-of-Thought through Entity Relationship AnalysisCode2
GreaseLM: Graph REASoning Enhanced Language Models for Question AnsweringCode2
Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-ImprovementCode2
An Embodied Generalist Agent in 3D WorldCode2
VHM: Versatile and Honest Vision Language Model for Remote Sensing Image AnalysisCode2
End-To-End Memory NetworksCode2
EmbodiedEval: Evaluate Multimodal LLMs as Embodied AgentsCode2
Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-LearningCode2
How Much are Large Language Models Contaminated? A Comprehensive Survey and the LLMSanitize LibraryCode2
End-to-End Navigation with Vision Language Models: Transforming Spatial Reasoning into Question-AnsweringCode2
Hungry Hungry Hippos: Towards Language Modeling with State Space ModelsCode2
FinBERT-QA: Financial Question Answering with pre-trained BERT Language ModelsCode2
Language Agent Tree Search Unifies Reasoning Acting and Planning in Language ModelsCode2
Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language ModelsCode2
Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement LearningCode2
MiniGPT-Med: Large Language Model as a General Interface for Radiology DiagnosisCode2
ISR-DPO: Aligning Large Multimodal Models for Videos by Iterative Self-Retrospective DPOCode2
JourneyDB: A Benchmark for Generative Image UnderstandingCode2
CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual ScenesCode1
EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language UnderstandingCode1
Causal Distillation for Language ModelsCode1
EgoTaskQA: Understanding Human Tasks in Egocentric VideosCode1
AllenAct: A Framework for Embodied AI ResearchCode1
Efficient Passage Retrieval with Hashing for Open-domain Question AnsweringCode1
MatTools: Benchmarking Large Language Models for Materials Science ToolsCode1
EgoTextVQA: Towards Egocentric Scene-Text Aware Video Question AnsweringCode1
Carpe Diem: On the Evaluation of World Knowledge in Lifelong Language ModelsCode1
CARE: Collaborative AI-Assisted Reading EnvironmentCode1
Efficiently Tuned Parameters are Task EmbeddingsCode1
Capturing Row and Column Semantics in Transformer Based Question Answering over TablesCode1
Adaptive Information Seeking for Open-Domain Question AnsweringCode1
Beyond NED: Fast and Effective Search Space Reduction for Complex Question Answering over Knowledge BasesCode1
Effective Human-AI Teams via Learned Natural Language Rules and OnboardingCode1
Structure-aware Domain Knowledge Injection for Large Language ModelsCode1
Ranked Voting based Self-Consistency of Large Language ModelsCode1
Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-Centric SummarizationCode1
Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented GenerationCode1
EgoToM: Benchmarking Theory of Mind Reasoning from Egocentric VideosCode1
Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language ModelCode1
AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-TuningCode1
Can't Remember Details in Long Documents? You Need Some R&RCode1
Adapting Pretrained Text-to-Text Models for Long Text SequencesCode1
EgoThink: Evaluating First-Person Perspective Thinking Capability of Vision-Language ModelsCode1
Can questions summarize a corpus? Using question generation for characterizing COVID-19 researchCode1
Can Question Rewriting Help Conversational Question Answering?Code1
ECBench: Can Multi-modal Foundation Models Understand the Egocentric World? A Holistic Embodied Cognition BenchmarkCode1
Can NLI Models Verify QA Systems’ Predictions?Code1
Benchmarking Multimodal Mathematical Reasoning with Explicit Visual DependencyCode1
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?Code1
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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