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 826–850 of 14297 papers

TitleStatusHype
LongGenBench: Long-context Generation BenchmarkCode1
Enriching Music Descriptions with a Finetuned-LLM and Metadata for Text-to-Music RetrievalCode1
Inductive Generative Recommendation via Retrieval-based SpeculationCode1
Adversarial Decoding: Generating Readable Documents for Adversarial ObjectivesCode1
Saliency-Guided DETR for Moment Retrieval and Highlight DetectionCode1
PairDistill: Pairwise Relevance Distillation for Dense RetrievalCode1
IFCap: Image-like Retrieval and Frequency-based Entity Filtering for Zero-shot CaptioningCode1
Exploring Hint Generation Approaches in Open-Domain Question AnsweringCode1
SpaGBOL: Spatial-Graph-Based Orientated LocalisationCode1
Boosting Healthcare LLMs Through Retrieved ContextCode1
Efficient and Discriminative Image Feature Extraction for Universal Image RetrievalCode1
ShizishanGPT: An Agricultural Large Language Model Integrating Tools and ResourcesCode1
Contextual Compression in Retrieval-Augmented Generation for Large Language Models: A SurveyCode1
Familiarity-Aware Evidence Compression for Retrieval-Augmented GenerationCode1
MoRAG -- Multi-Fusion Retrieval Augmented Generation for Human MotionCode1
Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented GenerationCode1
Trustworthiness in Retrieval-Augmented Generation Systems: A SurveyCode1
AudioBERT: Audio Knowledge Augmented Language ModelCode1
Weakly-supervised Camera Localization by Ground-to-satellite Image RegistrationCode1
RIRAG: Regulatory Information Retrieval and Answer GenerationCode1
M3-Jepa: Multimodal Alignment via Multi-directional MoE based on the JEPA frameworkCode1
A Survey of Multimodal Composite Editing and RetrievalCode1
NUDGE: Lightweight Non-Parametric Fine-Tuning of Embeddings for RetrievalCode1
RouterRetriever: Routing over a Mixture of Expert Embedding ModelsCode1
Pooling And Attention: What Are Effective Designs For LLM-Based Embedding Models?Code1
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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