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 1451–1475 of 14297 papers

TitleStatusHype
EdgeRAG: Online-Indexed RAG for Edge Devices—0
Language-based Audio Retrieval with Co-Attention Networks—0
TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting—0
Unsupervised dense retrieval with conterfactual contrastive learning—0
Retrieval-Augmented Generation for Mobile Edge Computing via Large Language Model—0
Towards Identity-Aware Cross-Modal Retrieval: a Dataset and a BaselineCode0
Length-Aware DETR for Robust Moment RetrievalCode1
Enhanced Multimodal RAG-LLM for Accurate Visual Question Answering—0
GliLem: Leveraging GliNER for Contextualized Lemmatization in Estonian—0
FashionFAE: Fine-grained Attributes Enhanced Fashion Vision-Language Pre-training—0
STAYKATE: Hybrid In-Context Example Selection Combining Representativeness Sampling and Retrieval-based Approach -- A Case Study on Science Domains—0
Long Context vs. RAG for LLMs: An Evaluation and RevisitsCode1
Optimizing Multi-Stage Language Models for Effective Text Retrieval—0
Semantic Residual for Multimodal Unified Discrete Representation—0
Jasper and Stella: distillation of SOTA embedding modelsCode1
ViPCap: Retrieval Text-Based Visual Prompts for Lightweight Image CaptioningCode0
RAG with Differential PrivacyCode1
Reversed in Time: A Novel Temporal-Emphasized Benchmark for Cross-Modal Video-Text RetrievalCode0
FOR: Finetuning for Object Level Open Vocabulary Image Retrieval—0
Research Experiment on Multi-Model Comparison for Chinese Text Classification Tasks—0
On the Robustness of Generative Information Retrieval ModelsCode0
DynaGRAG | Exploring the Topology of Information for Advancing Language Understanding and Generation in Graph Retrieval-Augmented Generation—0
Improving Factuality with Explicit Working Memory—0
Pirates of the RAG: Adaptively Attacking LLMs to Leak Knowledge Bases—0
RaSeRec: Retrieval-Augmented Sequential RecommendationCode1
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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