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 8901–8925 of 14297 papers

TitleStatusHype
Associative Memories Based on Multiple-Valued Sparse Clustered Networks—0
Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models—0
Fine Tuning LLM for Enterprise: Practical Guidelines and Recommendations—0
Comparing persistence diagrams through complex vectors—0
Fine-Tuning Large Language Models and Evaluating Retrieval Methods for Improved Question Answering on Building Codes—0
Comparing Neighbors Together Makes it Easy: Jointly Comparing Multiple Candidates for Efficient and Effective Retrieval—0
Fine-tuning Language Models for Factuality—0
Fine-tuning CLIP Text Encoders with Two-step Paraphrasing—0
Comparing Knowledge Sources for Open-Domain Scientific Claim Verification—0
Comparing Fair Ranking Metrics—0
A Language Model with Limited Memory Capacity Captures Interference in Human Sentence Processing—0
Fine-tuning and aligning question answering models for complex information extraction tasks—0
Compare and Reweight: Distinctive Image Captioning Using Similar Images Sets—0
Fine-Tuned Large Language Models for Symptom Recognition from Spanish Clinical Text—0
Comparator Networks—0
CLAREL: Classification via retrieval loss for zero-shot learning—0
Comparative study of Authorship Identification Techniques for Cyber Forensics Analysis—0
Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval—0
CaReBench: A Fine-Grained Benchmark for Video Captioning and Retrieval—0
Fine-grained Text-Video Retrieval with Frozen Image Encoders—0
Fine-Grained Sentence Functions for Short-Text Conversation—0
Comparative analysis of various web crawler algorithms—0
Fine-grained Retrieval Prompt Tuning—0
Fine-Grained Retrieval-Augmented Generation for Visual Question Answering—0
Comparative analysis of segmentation and generative models for fingerprint retrieval task—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