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 3126–3150 of 14297 papers

TitleStatusHype
Demo: Soccer Information Retrieval via Natural Queries using SoccerRAGCode0
R2C2-Coder: Enhancing and Benchmarking Real-world Repository-level Code Completion Abilities of Code Large Language Models—0
Session Context Embedding for Intent Understanding in Product Search—0
A Survey of Generative Information RetrievalCode0
Multi-word Term Embeddings Improve Lexical Product Retrieval—0
An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented GenerationCode1
BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models—0
Mixture of Rationale: Multi-Modal Reasoning Mixture for Visual Question Answering—0
Scale-Free Image Keypoints Using Differentiable Persistent HomologyCode1
A Theory for Token-Level Harmonization in Retrieval-Augmented Generation—0
Hybrid-Learning Video Moment Retrieval across Multi-Domain Labels—0
Luna: An Evaluation Foundation Model to Catch Language Model Hallucinations with High Accuracy and Low Cost—0
Decompose, Enrich, and Extract! Schema-aware Event Extraction using LLMs—0
COS-Mix: Cosine Similarity and Distance Fusion for Improved Information RetrievalCode4
Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented GenerationCode0
SPAGHETTI: Open-Domain Question Answering from Heterogeneous Data Sources with Retrieval and Semantic Parsing—0
RAG Does Not Work for Enterprises—0
Retrieval Meets Reasoning: Even High-school Textbook Knowledge Benefits Multimodal Reasoning—0
Don't Buy it! Reassessing the Ad Understanding Abilities of Contrastive Multimodal ModelsCode0
Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language ModelsCode0
Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial TrainingCode1
Predicting ptychography probe positions using single-shot phase retrieval neural networkCode0
ExU: AI Models for Examining Multilingual Disinformation Narratives and Understanding their Spread—0
GNN-RAG: Graph Neural Retrieval for Large Language Model ReasoningCode3
Phantom: General Trigger Attacks on Retrieval Augmented Language Generation—0
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All datasetsQuora Question PairsHotpotQANatural QuestionsOK-VQAInfoSeekMVKPolyvorePubMedQAPubMedQA corpus with metadataToolLensคลิปไวรัล!! ไอซ์ ปรีชญา ลืมปิดไลฟ์สดตอนอาบน้ำ ถูกแชร์กระหึ่มเน็ต

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