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 8876–8900 of 14297 papers

TitleStatusHype
First Token Probability Guided RAG for Telecom Question Answering—0
Compensating for Large In-Plane Rotations in Natural Images—0
A Stable Multi-Scale Kernel for Topological Machine Learning—0
First Steps towards the Semi-automatic Development of a Wordformation-based Lexicon of Latin—0
First Place Solution of 2023 Global Artificial Intelligence Technology Innovation Competition Track 1—0
Compatibility-aware Heterogeneous Visual Search—0
Firing Rate Models as Associative Memory: Excitatory-Inhibitory Balance for Robust Retrieval—0
Comparison Research of Millimeter-Wave/Infrared Co-aperture Reflector Antenna Systems Based on a Specialized Film—0
FinTMMBench: Benchmarking Temporal-Aware Multi-Modal RAG in Finance—0
FinTextQA: A Dataset for Long-form Financial Question Answering—0
Comparison of the effects of investor attention using search volume data before and after mobile device popularization—0
Investigation of Singing Voice Separation for Singing Voice Detection in Polyphonic Music—0
Associative Memories via Predictive Coding—0
Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval—0
A Bayesian Nonparametric Topic Model with Variational Auto-Encoders—0
FinSage: A Multi-aspect RAG System for Financial Filings Question Answering—0
FinRAGBench-V: A Benchmark for Multimodal RAG with Visual Citation in the Financial Domain—0
FinLoRA: Finetuning Quantized Financial Large Language Models Using Low-Rank Adaptation—0
Comparing Traditional and LLM-based Search for Image Geolocation—0
Associative Memories to Accelerate Approximate Nearest Neighbor Search—0
Fine-tuning the SwissBERT Encoder Model for Embedding Sentences and Documents—0
Fine-Tuning the Retrieval Mechanism for Tabular Deep Learning—0
Fine-tuning Pre-trained Named Entity Recognition Models For Indian Languages—0
Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs—0
Comparing Probabilistic, Distributional and Transformer-Based Models on Logical Metonymy Interpretation—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