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 13801–13850 of 14297 papers

TitleStatusHype
REAPER: Reasoning based Retrieval Planning for Complex RAG Systems—0
RAGged Edges: The Double-Edged Sword of Retrieval-Augmented Chatbots—0
RAG-Fusion: a New Take on Retrieval-Augmented Generation—0
RAG for Effective Supply Chain Security Questionnaire Automation—0
RAGent: Retrieval-based Access Control Policy Generation—0
RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning—0
ReasonChainQA: Text-based Complex Question Answering with Explainable Evidence Chains—0
Reason first, then respond: Modular Generation for Knowledge-infused Dialogue—0
Reasoning about Fine-grained Attribute Phrases using Reference Games—0
Reasoning Beyond Limits: Advances and Open Problems for LLMs—0
Reasoning Court: Combining Reasoning, Action, and Judgment for Multi-Hop Reasoning—0
Natural Language Interaction with a Household Electricity Knowledge-based Digital Twin—0
Using Centroids of Word Embeddings and Word Mover's Distance for Biomedical Document Retrieval in Question Answering—0
Using consumer feedback from location-based services in PoI recommender systems for people with autism—0
Using Convex Optimization of Autocorrelation with Constrained Support and Windowing for Improved Phase Retrieval Accuracy—0
RAGE Against the Machine: Retrieval-Augmented LLM Explanations—0
RAG Does Not Work for Enterprises—0
Using Deep Cross Modal Hashing and Error Correcting Codes for Improving the Efficiency of Attribute Guided Facial Image Retrieval—0
ReasoningRank: Teaching Student Models to Rank through Reasoning-Based Knowledge Distillation—0
Attribution in Scientific Literature: New Benchmark and Methods—0
Reassessing the Limitations of CNN Methods for Camera Pose Regression—0
RE: A Study for Restorable Embeddings—0
Rebalanced Vision-Language Retrieval Considering Structure-Aware Distillation—0
Using EEG Signals to Assess Workload during Memory Retrieval in a Real-world Scenario—0
RAGDiffusion: Faithful Cloth Generation via External Knowledge Assimilation—0
Using Entropy Estimates for DAG-Based Ontologies—0
Recall: Empowering Multimodal Embedding for Edge Devices—0
Recall is the Proper Evaluation Metric for Word Segmentation—0
RAG-Check: Evaluating Multimodal Retrieval Augmented Generation Performance—0
Recall, Retrieve and Reason: Towards Better In-Context Relation Extraction—0
Recall Them All: Retrieval-Augmented Language Models for Long Object List Extraction from Long Documents—0
RAGCache: Efficient Knowledge Caching for Retrieval-Augmented Generation—0
Using Generic Summarization to Improve Music Information Retrieval Tasks—0
RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems—0
RAG-based Question Answering over Heterogeneous Data and Text—0
Recency Ranking by Diversification of Result Set—0
Recent Advance in Content-based Image Retrieval: A Literature Survey—0
Recent advances in text embedding: A Comprehensive Review of Top-Performing Methods on the MTEB Benchmark—0
Recent Advances in Video Question Answering: A Review of Datasets and Methods—0
RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models—0
Recherche et extraction d'information dans des cas cliniques. Pr\'esentation de la campagne d'\'evaluation DEFT 2019 (Information Retrieval and Information Extraction from Clinical Cases)—0
Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images—0
RAGAR: Retrieval Augment Personalized Image Generation Guided by Recommendation—0
Reciprocal Learning of Knowledge Retriever and Response Ranker for Knowledge-Grounded Conversations—0
Rag and Roll: An End-to-End Evaluation of Indirect Prompt Manipulations in LLM-based Application Frameworks—0
RAG-6DPose: Retrieval-Augmented 6D Pose Estimation via Leveraging CAD as Knowledge Base—0
RECLIP: Resource-efficient CLIP by Training with Small Images—0
Recognition-free Question Answering on Handwritten Document Collections—0
Recognizing and Presenting the Storytelling Video Structure with Deep Multimodal Networks—0
Recognizing Chinese Judicial Named Entity using BiLSTM-CRF—0
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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