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 13901–13950 of 14297 papers

TitleStatusHype
Using Multiple Instance Learning to Build Multimodal Representations—0
Referring Expression Instance Retrieval and A Strong End-to-End Baseline—0
Using Pretrained Large Language Model with Prompt Engineering to Answer Biomedical Questions—0
REFIND: Retrieval-Augmented Factuality Hallucination Detection in Large Language Models—0
REFINE on Scarce Data: Retrieval Enhancement through Fine-Tuning via Model Fusion of Embedding Models—0
Using Rank Aggregation for Expert Search in Academic Digital Libraries—0
Refining Knowledge Transfer on Audio-Image Temporal Agreement for Audio-Text Cross Retrieval—0
RACon: Retrieval-Augmented Simulated Character Locomotion Control—0
RA-CLIP: Retrieval Augmented Contrastive Language-Image Pre-Training—0
RAC3: Retrieval-Augmented Corner Case Comprehension for Autonomous Driving with Vision-Language Models—0
RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training—0
Reflection Invariant and Symmetry Detection—0
Reflector Antennas Characterization and Diagnostics using a Single Set of Far Field Phaseless Data and Crosswords-like Processing—0
Refocusing on Relevance: Personalization in NLG—0
Reformulating Information Retrieval from Speech and Text as a Detection Problem—0
Reframing Tax Law Entailment as Analogical Reasoning—0
R4: Reinforced Retriever-Reorder-Responder for Retrieval-Augmented Large Language Models—0
REGen: Multimodal Retrieval-Embedded Generation for Long-to-Short Video Editing—0
REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context in New Environments—0
R3 : Refined Retriever-Reader pipeline for Multidoc2dial—0
Region and Location Based Indexing and Retrieval of MR-T2 Brain Tumor Images—0
Region-Aware Face Swapping—0
Region-aware Image-based Human Action Retrieval with Transformers—0
R^3Mem: Bridging Memory Retention and Retrieval via Reversible Compression—0
Region-based Contrastive Pretraining for Medical Image Retrieval with Anatomic Query—0
Region-Based Image Retrieval Revisited—0
R^3AG: First Workshop on Refined and Reliable Retrieval Augmented Generation—0
Region Convolutional Features for Multi-Label Remote Sensing Image Retrieval—0
Register assisted aggregation for Visual Place Recognition—0
R2GAN: Cross-Modal Recipe Retrieval With Generative Adversarial Network—0
Regret Minimization in Stochastic Contextual Dueling Bandits—0
Regular expressions for decoding of neural network outputs—0
Regularized Autoencoders for Isometric Representation Learning—0
Regularized Contrastive Learning of Semantic Search—0
R2-Diff: Denoising by diffusion as a refinement of retrieved motion for image-based motion prediction—0
Regularizing Deep Hashing Networks Using GAN Generated Fake Images—0
Regulatory Compliance through Doc2Doc Information Retrieval: A case study in EU/UK legislation where text similarity has limitations—0
R2C2-Coder: Enhancing and Benchmarking Real-world Repository-level Code Completion Abilities of Code Large Language Models—0
Re-Imagen: Retrieval-Augmented Text-to-Image Generator—0
Reimagining Retrieval Augmented Language Models for Answering Queries—0
Reimagining Target-Aware Molecular Generation through Retrieval-Enhanced Aligned Diffusion—0
Reinforced Information Retrieval—0
Qwen2.5-32B: Leveraging Self-Consistent Tool-Integrated Reasoning for Bengali Mathematical Olympiad Problem Solving—0
Reinforced Question Rewriting for Conversational Question Answering—0
Reinforcement Learning-driven Information Seeking: A Quantum Probabilistic Approach—0
Using Score Distributions to Compare Statistical Significance Tests for Information Retrieval Evaluation—0
Reinforcement Learning for Optimizing RAG for Domain Chatbots—0
Reinforcement Learning for Weakly Supervised Temporal Grounding of Natural Language in Untrimmed Videos—0
Reinforcement Learning to Rank with Coarse-grained Labels—0
Using Semantics for Granularities of Tokenization—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