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 5476–5500 of 14297 papers

TitleStatusHype
Polyglot or Not? Measuring Multilingual Encyclopedic Knowledge in Foundation ModelsCode1
WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on WikipediaCode3
IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions—0
EDIS: Entity-Driven Image Search over Multimodal Web ContentCode1
i-Code Studio: A Configurable and Composable Framework for Integrative AI—0
When the Music Stops: Tip-of-the-Tongue Retrieval for MusicCode0
CTQScorer: Combining Multiple Features for In-context Example Selection for Machine TranslationCode0
SciMON: Scientific Inspiration Machines Optimized for NoveltyCode1
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text GenerationCode2
Dr.ICL: Demonstration-Retrieved In-context Learning—0
Differential Privacy with Random Projections and Sign Random Projections—0
REFinD: Relation Extraction Financial DatasetCode0
A Comprehensive Survey of Sentence Representations: From the BERT Epoch to the ChatGPT Era and Beyond—0
Knowledge-Retrieval Task-Oriented Dialog Systems with Semi-SupervisionCode0
Retrieval-augmented Multi-label Text Classification—0
ConQueR: Contextualized Query Reduction using Search LogsCode0
Nearest Neighbor Machine Translation is Meta-Optimizer on Output Projection LayerCode0
Challenging Decoder helps in Masked Auto-Encoder Pre-training for Dense Passage Retrieval—0
Text-based Person Search without Parallel Image-Text Data—0
VLAB: Enhancing Video Language Pre-training by Feature Adapting and Blending—0
Crosslingual Transfer Learning for Low-Resource Languages Based on Multilingual Colexification GraphsCode0
Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge ConflictsCode1
Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical StudyCode1
Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse ApproachCode0
Materialistic: Selecting Similar Materials in Images—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