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 2650 of 10817 papers

TitleStatusHype
DSPy: Compiling Declarative Language Model Calls into Self-Improving PipelinesCode7
LLaMA: Open and Efficient Foundation Language ModelsCode7
Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPCode7
Mistral 7BCode6
LongLoRA: Efficient Fine-tuning of Long-Context Large Language ModelsCode6
h2oGPT: Democratizing Large Language ModelsCode6
RET-LLM: Towards a General Read-Write Memory for Large Language ModelsCode6
Pythia: A Suite for Analyzing Large Language Models Across Training and ScalingCode6
GPT-4 Technical ReportCode6
Automatic Chain of Thought Prompting in Large Language ModelsCode6
Training Compute-Optimal Large Language ModelsCode6
Training language models to follow instructions with human feedbackCode6
Chain-of-Thought Prompting Elicits Reasoning in Large Language ModelsCode6
RAG-R1 : Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query ParallelismCode5
Continuous Thought MachinesCode5
Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble ScorersCode5
Pixel-SAIL: Single Transformer For Pixel-Grounded UnderstandingCode5
TrustRAG: An Information Assistant with Retrieval Augmented GenerationCode5
Search-o1: Agentic Search-Enhanced Large Reasoning ModelsCode5
KBLaM: Knowledge Base augmented Language ModelCode5
Show-o: One Single Transformer to Unify Multimodal Understanding and GenerationCode5
BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive RetrievalCode5
VisionLLM v2: An End-to-End Generalist Multimodal Large Language Model for Hundreds of Vision-Language TasksCode5
VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMsCode5
Wings: Learning Multimodal LLMs without Text-only ForgettingCode5
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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