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 151–200 of 10817 papers

TitleStatusHype
A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and MultimodalCode3
A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and TrustworthinessCode3
Attention Is All You NeedCode3
EgoLife: Towards Egocentric Life AssistantCode3
Retrieval Augmented Generation and Understanding in Vision: A Survey and New OutlookCode3
3D-LLM: Injecting the 3D World into Large Language ModelsCode3
DriveLM: Driving with Graph Visual Question AnsweringCode3
Prompting Is Programming: A Query Language for Large Language ModelsCode3
PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal RetrieversCode3
PCToolkit: A Unified Plug-and-Play Prompt Compression Toolkit of Large Language ModelsCode3
Ai2 Scholar QA: Organized Literature Synthesis with AttributionCode3
ST-MoE: Designing Stable and Transferable Sparse Expert ModelsCode3
Detecting hallucinations in large language models using semantic entropyCode3
ONE-PEACE: Exploring One General Representation Model Toward Unlimited ModalitiesCode3
All You May Need for VQA are Image CaptionsCode3
Odyssey: Empowering Minecraft Agents with Open-World SkillsCode3
Evaluating Hallucinations in Chinese Large Language ModelsCode3
Self-QA: Unsupervised Knowledge Guided Language Model AlignmentCode3
CRAG -- Comprehensive RAG BenchmarkCode3
MLLMs Know Where to Look: Training-free Perception of Small Visual Details with Multimodal LLMsCode3
CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language ModelsCode3
MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of ExpertsCode3
Meta-Chunking: Learning Text Segmentation and Semantic Completion via Logical PerceptionCode3
MDocAgent: A Multi-Modal Multi-Agent Framework for Document UnderstandingCode3
RAGEval: Scenario Specific RAG Evaluation Dataset Generation FrameworkCode3
Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal ModelsCode3
LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive MemoryCode3
M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language ModelsCode3
Longformer: The Long-Document TransformerCode3
A Survey of Large Language Models in Finance (FinLLMs)Code3
Reinforcement Learning Outperforms Supervised Fine-Tuning: A Case Study on Audio Question AnsweringCode3
ReMEmbR: Building and Reasoning Over Long-Horizon Spatio-Temporal Memory for Robot NavigationCode3
MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video UnderstandingCode3
Champion Solution for the WSDM2023 Toloka VQA ChallengeCode3
LLaMA-Omni2: LLM-based Real-time Spoken Chatbot with Autoregressive Streaming Speech SynthesisCode3
L0: Reinforcement Learning to Become General AgentsCode3
ERNIE 2.0: A Continual Pre-training Framework for Language UnderstandingCode3
Language Models are Few-Shot LearnersCode3
CAD-Recode: Reverse Engineering CAD Code from Point CloudsCode3
KVzip: Query-Agnostic KV Cache Compression with Context ReconstructionCode3
Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-ThoughtCode3
Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question ComplexityCode3
Husky: A Unified, Open-Source Language Agent for Multi-Step ReasoningCode3
Impromptu VLA: Open Weights and Open Data for Driving Vision-Language-Action ModelsCode3
BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingCode3
SVIT: Scaling up Visual Instruction TuningCode3
Evaluating Large Language Models with fmevalCode3
Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning AgentCode3
DARWIN 1.5: Large Language Models as Materials Science Adapted LearnersCode3
Hawk: Learning to Understand Open-World Video AnomaliesCode3
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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