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 6801–6825 of 14297 papers

TitleStatusHype
IRNLP_DAIICT@DravidianLangTech-EACL2021:Offensive Language identification in Dravidian Languages using TF-IDF Char N-grams and MuRIL—0
IRNLP_DAIICT@LT-EDI-EACL2021: Hope Speech detection in Code Mixed text using TF-IDF Char N-grams and MuRIL—0
Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation—0
Exploiting CLIP-based Multi-modal Approach for Artwork Classification and Retrieval—0
ISA-bEL: Intelligent Search Algorithm based on Entity Linking—0
Deep Aggregation of Regional Convolutional Activations for Content Based Image Retrieval—0
Exploiting Citation Knowledge in Personalised Recommendation of Recent Scientific Publications—0
Is Cross-lingual Evaluation Only About Cross-lingual?—0
Is Cross-modal Information Retrieval Possible without Training?—0
Exploiting Bilingual Translation for Question Retrieval in Community-Based Question Answering—0
ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphs—0
Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning—0
Is Image Encoding Beneficial for Deep Learning in Finance? An Analysis of Image Encoding Methods for the Application of Convolutional Neural Networks in Finance—0
iSimLoc: Visual Global Localization for Previously Unseen Environments with Simulated Images—0
CLIP-GCD: Simple Language Guided Generalized Category Discovery—0
A Riemannian Framework for Analysis of Human Body Surface—0
Explanations for CommonsenseQA: New Dataset and Models—0
Is My Data in Your Retrieval Database? Membership Inference Attacks Against Retrieval Augmented Generation—0
CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not—0
IsOBS: An Information System for Oracle Bone Script—0
Deep Binaries: Encoding Semantic-Rich Cues for Efficient Textual-Visual Cross Retrieval—0
Is Relevance Propagated from Retriever to Generator in RAG?—0
Is Semantic Chunking Worth the Computational Cost?—0
Is Table Retrieval a Solved Problem? Exploring Join-Aware Multi-Table Retrieval—0
100,000 Podcasts: A Spoken English Document Corpus—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