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 150 of 10817 papers

TitleStatusHype
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement LearningCode15
From Local to Global: A Graph RAG Approach to Query-Focused SummarizationCode13
SWIFT:A Scalable lightWeight Infrastructure for Fine-TuningCode11
WebWalker: Benchmarking LLMs in Web TraversalCode11
Visually Descriptive Language Model for Vector Graphics ReasoningCode9
KAG: Boosting LLMs in Professional Domains via Knowledge Augmented GenerationCode9
BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-HaystackCode9
DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal UnderstandingCode9
Llama 2: Open Foundation and Fine-Tuned Chat ModelsCode8
LLM-AutoDiff: Auto-Differentiate Any LLM WorkflowCode7
MiniGPT-v2: large language model as a unified interface for vision-language multi-task learningCode7
Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPCode7
MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge DiscoveryCode7
DSPy: Compiling Declarative Language Model Calls into Self-Improving PipelinesCode7
TextGrad: Automatic "Differentiation" via TextCode7
Lumina-mGPT: Illuminate Flexible Photorealistic Text-to-Image Generation with Multimodal Generative PretrainingCode7
HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language ModelsCode7
Kimi-Audio Technical ReportCode7
GLM-4-Voice: Towards Intelligent and Human-Like End-to-End Spoken ChatbotCode7
Ichigo: Mixed-Modal Early-Fusion Realtime Voice AssistantCode7
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement LearningCode7
Chameleon: Mixed-Modal Early-Fusion Foundation ModelsCode7
When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language ModelsCode7
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and PlanningCode7
LLaMA: Open and Efficient Foundation Language ModelsCode7
LLaVA-CoT: Let Vision Language Models Reason Step-by-StepCode7
Scaling Speech-Text Pre-training with Synthetic Interleaved DataCode7
PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning MethodsCode7
Training Compute-Optimal Large Language ModelsCode6
Training language models to follow instructions with human feedbackCode6
LongLoRA: Efficient Fine-tuning of Long-Context Large Language ModelsCode6
Chain-of-Thought Prompting Elicits Reasoning in 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
h2oGPT: Democratizing Large Language ModelsCode6
Mistral 7BCode6
RET-LLM: Towards a General Read-Write Memory for Large Language ModelsCode6
TextMonkey: An OCR-Free Large Multimodal Model for Understanding DocumentCode5
Tree of Thoughts: Deliberate Problem Solving with Large Language ModelsCode5
TrustRAG: An Information Assistant with Retrieval Augmented GenerationCode5
Continuous Thought MachinesCode5
Show-o: One Single Transformer to Unify Multimodal Understanding and GenerationCode5
BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive RetrievalCode5
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in MedicineCode5
KBLaM: Knowledge Base augmented Language ModelCode5
Search-o1: Agentic Search-Enhanced Large Reasoning ModelsCode5
Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and BeyondCode5
RAG-R1 : Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query ParallelismCode5
Pixel-SAIL: Single Transformer For Pixel-Grounded UnderstandingCode5
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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