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 4476–4500 of 14297 papers

TitleStatusHype
Hubless Nearest Neighbor Search for Bilingual Lexicon InductionCode0
HRDE: Retrieval-Augmented Large Language Models for Chinese Health Rumor Detection and ExplainabilityCode0
How Train-Test Leakage Affects Zero-shot RetrievalCode0
Human-in-the-loop Machine Translation with Large Language ModelCode0
Challenging Neural Dialogue Models with Natural Data: Memory Networks Fail on Incremental PhenomenaCode0
How “Loco” Is the LOCO Corpus? Annotating the Language of Conspiracy TheoriesCode0
Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVMCode0
How should we evaluate supervised hashing?Code0
How to Leverage Personal Textual Knowledge for Personalized Conversational Information RetrievalCode0
Reverse-Engineering the Retrieval Process in GenIR ModelsCode0
Inter-intra Variant Dual Representations forSelf-supervised Video RecognitionCode0
Challenges in Generalization in Open Domain Question AnsweringCode0
Challenge Dataset of Cognates and False Friend Pairs from Indian LanguagesCode0
A Quick, trustworthy spectral knowledge Q&A system leveraging retrieval-augmented generation on LLMCode0
How Language-Neutral is Multilingual BERT?Code0
How Different are Pre-trained Transformers for Text Ranking?Code0
A Question-Entailment Approach to Question AnsweringCode0
Chain of Thought Prompting Elicits Knowledge AugmentationCode0
A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase GenerationCode0
Agent-Enhanced Large Language Models for Researching Political InstitutionsCode0
A Quadruplet Loss for Enforcing Semantically Coherent Embeddings in Multi-output Classification ProblemsCode0
HOTTER: Hierarchical Optimal Topic Transport with Explanatory Context RepresentationsCode0
How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image RetrievalCode0
How convolutional neural network see the world - A survey of convolutional neural network visualization methodsCode0
Chain-of-Exemplar: Enhancing Distractor Generation for Multimodal Educational Question GenerationCode0
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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