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

Showing 426450 of 10817 papers

TitleStatusHype
Pengi: An Audio Language Model for Audio TasksCode2
Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question AnsweringCode2
Automated Evaluation of Retrieval-Augmented Language Models with Task-Specific Exam GenerationCode2
PG-Video-LLaVA: Pixel Grounding Large Video-Language ModelsCode2
Fine-Grained Human Feedback Gives Better Rewards for Language Model TrainingCode2
FlagEvalMM: A Flexible Framework for Comprehensive Multimodal Model EvaluationCode2
Atlas: Few-shot Learning with Retrieval Augmented Language ModelsCode2
FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language ModelsCode2
EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysisCode2
Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerCode2
AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction SimulatorCode2
FakeBench: Probing Explainable Fake Image Detection via Large Multimodal ModelsCode2
AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous DrivingCode2
FinBERT-QA: Financial Question Answering with pre-trained BERT Language ModelsCode2
Evaluating LLM Reasoning in the Operations Research Domain with ORQACode2
Evaluating RAG-Fusion with RAGElo: an Automated Elo-based FrameworkCode2
QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training QuantizationCode2
E.T. Bench: Towards Open-Ended Event-Level Video-Language UnderstandingCode2
Analyzing and Boosting the Power of Fine-Grained Visual Recognition for Multi-modal Large Language ModelsCode2
ERA-CoT: Improving Chain-of-Thought through Entity Relationship AnalysisCode2
Breaking the Ceiling of the LLM Community by Treating Token Generation as a Classification for EnsemblingCode2
End-to-End Navigation with Vision Language Models: Transforming Spatial Reasoning into Question-AnsweringCode2
Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-ImprovementCode2
Explore the Limits of Omni-modal Pretraining at ScaleCode2
Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based RetrieversCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1IE-Net (ensemble)EM90.94Unverified
2FPNet (ensemble)EM90.87Unverified
3IE-NetV2 (ensemble)EM90.86Unverified
4SA-Net on Albert (ensemble)EM90.72Unverified
5SA-Net-V2 (ensemble)EM90.68Unverified
6FPNet (ensemble)EM90.6Unverified
7Retro-Reader (ensemble)EM90.58Unverified
8EntitySpanFocusV2 (ensemble)EM90.52Unverified
9TransNets + SFVerifier + SFEnsembler (ensemble)EM90.49Unverified
10EntitySpanFocus+AT (ensemble)EM90.45Unverified