SOTAVerified

Retrieval

A methodology that involves selecting relevant data or examples from a large dataset to support tasks like prediction, learning, or inference. It enhances models by providing context or additional information, often used in systems like retrieval-augmented generation or in-context learning.

Papers

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Showing 176–200 of 14297 papers

TitleStatusHype
Developing Generalist Foundation Models from a Multimodal Dataset for 3D Computed TomographyCode3
Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question ComplexityCode3
AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain FrameworkCode3
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon GenerationCode3
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextCode3
Bridging Language and Items for Retrieval and RecommendationCode3
PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal RetrieversCode3
PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language ModelsCode3
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning TasksCode3
When Large Language Models Meet Vector Databases: A SurveyCode3
CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language ModelsCode3
Corrective Retrieval Augmented GenerationCode3
MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop QueriesCode3
INTERS: Unlocking the Power of Large Language Models in Search with Instruction TuningCode3
FinanceBench: A New Benchmark for Financial Question AnsweringCode3
Large Language Model based Long-tail Query Rewriting in Taobao SearchCode3
WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human PreferencesCode3
Landmark Attention: Random-Access Infinite Context Length for TransformersCode3
WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on WikipediaCode3
ONE-PEACE: Exploring One General Representation Model Toward Unlimited ModalitiesCode3
AToMiC: An Image/Text Retrieval Test Collection to Support Multimedia Content CreationCode3
Universal Instance Perception as Object Discovery and RetrievalCode3
REPLUG: Retrieval-Augmented Black-Box Language ModelsCode3
Vision-Language Pre-training: Basics, Recent Advances, and Future TrendsCode3
A GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation ModelsCode3
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second183.53—Unverified
2ElasticsearchQueries per second21.8—Unverified
3BM25-PTQueries per second6.49—Unverified
4Rank-BM25Queries per second1.18—Unverified
#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second20.88—Unverified
2ElasticsearchQueries per second7.11—Unverified
3Rank-BM25Queries per second0.04—Unverified
#ModelMetricClaimedVerifiedStatus
1BM25SQueries per second41.85—Unverified
2ElasticsearchQueries per second12.16—Unverified
3Rank-BM25Queries per second0.1—Unverified
#ModelMetricClaimedVerifiedStatus
1FLMRRecall@589.32—Unverified
2RA-VQARecall@582.84—Unverified
#ModelMetricClaimedVerifiedStatus
1PreFLMRRecall@562.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CLIP-KIStext-to-video Mean Rank30—Unverified
#ModelMetricClaimedVerifiedStatus
1CLIP4OutfitRecall@57.59—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAGAccuracy (Top-1)82.1—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAGAccuracy (Top-1)82.1—Unverified
#ModelMetricClaimedVerifiedStatus
1COLTCOMP@84.55—Unverified
#ModelMetricClaimedVerifiedStatus
1hello0L1,121,222—Unverified