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 176–200 of 10817 papers

TitleStatusHype
Ai2 Scholar QA: Organized Literature Synthesis with AttributionCode3
Attention Is All You NeedCode3
A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and TrustworthinessCode3
LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive MemoryCode3
Efficient Multimodal Large Language Models: A SurveyCode3
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
Longformer: The Long-Document TransformerCode3
LLaMA-Omni2: LLM-based Real-time Spoken Chatbot with Autoregressive Streaming Speech SynthesisCode3
Champion Solution for the WSDM2023 Toloka VQA ChallengeCode3
MDocAgent: A Multi-Modal Multi-Agent Framework for Document UnderstandingCode3
Language Models are Few-Shot LearnersCode3
ERNIE 2.0: A Continual Pre-training Framework for Language UnderstandingCode3
Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-ThoughtCode3
CAD-Recode: Reverse Engineering CAD Code from Point CloudsCode3
L0: Reinforcement Learning to Become General AgentsCode3
InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic ChartsCode3
KVzip: Query-Agnostic KV Cache Compression with Context ReconstructionCode3
LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding Reasoning and PlanningCode3
Husky: A Unified, Open-Source Language Agent for Multi-Step ReasoningCode3
Hawk: Learning to Understand Open-World Video AnomaliesCode3
Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question ComplexityCode3
Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning AgentCode3
BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingCode3
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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