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 101–150 of 10817 papers

TitleStatusHype
Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question AnsweringCode4
A Survey on Vision-Language-Action Models for Embodied AICode4
Knowledge Fusion of Large Language ModelsCode4
Scaling Up Biomedical Vision-Language Models: Fine-Tuning, Instruction Tuning, and Multi-Modal LearningCode4
N-Grammer: Augmenting Transformers with latent n-gramsCode4
OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and ReasoningCode4
SQuARE: Sequential Question Answering Reasoning Engine for Enhanced Chain-of-Thought in Large Language ModelsCode4
VITA-Audio: Fast Interleaved Cross-Modal Token Generation for Efficient Large Speech-Language ModelCode4
A Survey of Large Language Models in Finance (FinLLMs)Code3
Retrieval Augmented Generation and Understanding in Vision: A Survey and New OutlookCode3
REPLUG: Retrieval-Augmented Black-Box Language ModelsCode3
EgoLife: Towards Egocentric Life AssistantCode3
A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and MultimodalCode3
SALMONN: Towards Generic Hearing Abilities for Large Language ModelsCode3
Efficient Multimodal Large Language Models: A SurveyCode3
Prompting Is Programming: A Query Language for Large Language ModelsCode3
Q-Bench+: A Benchmark for Multi-modal Foundation Models on Low-level Vision from Single Images to PairsCode3
A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and TrustworthinessCode3
Generating Long Sequences with Sparse TransformersCode3
ERNIE: Enhanced Representation through Knowledge IntegrationCode3
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
Scaling Instruction-Finetuned Language ModelsCode3
ERNIE 2.0: A Continual Pre-training Framework for Language UnderstandingCode3
PCToolkit: A Unified Plug-and-Play Prompt Compression Toolkit of Large Language ModelsCode3
DriveLM: Driving with Graph Visual Question AnsweringCode3
ONE-PEACE: Exploring One General Representation Model Toward Unlimited ModalitiesCode3
Odyssey: Empowering Minecraft Agents with Open-World SkillsCode3
Detecting hallucinations in large language models using semantic entropyCode3
MLLMs Know Where to Look: Training-free Perception of Small Visual Details with Multimodal LLMsCode3
DARWIN 1.5: Large Language Models as Materials Science Adapted LearnersCode3
Evaluating Hallucinations in Chinese Large Language ModelsCode3
ST-MoE: Designing Stable and Transferable Sparse Expert ModelsCode3
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
Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal ModelsCode3
PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal RetrieversCode3
MDocAgent: A Multi-Modal Multi-Agent Framework for Document UnderstandingCode3
M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language ModelsCode3
LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive MemoryCode3
MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video UnderstandingCode3
Longformer: The Long-Document TransformerCode3
CRAG -- Comprehensive RAG BenchmarkCode3
3D-LLM: Injecting the 3D World into Large Language ModelsCode3
LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding Reasoning and PlanningCode3
Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-ThoughtCode3
Language Models are Few-Shot LearnersCode3
LLaMA-Omni2: LLM-based Real-time Spoken Chatbot with Autoregressive Streaming Speech SynthesisCode3
AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain FrameworkCode3
KVzip: Query-Agnostic KV Cache Compression with Context ReconstructionCode3
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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