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 7251–7275 of 14297 papers

TitleStatusHype
Learning to Synthesize: Robust Phase Retrieval at Low Photon counts—0
Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks—0
DeepTopPush: Simple and Scalable Method for Accuracy at the Top—0
LawngNLI: a multigranular, long-premise NLI benchmark for evaluating models’ in-domain generalization from short to long contexts—0
Classifying Peace in Global Media Using RAG and Intergroup Reciprocity—0
LawSum: A weakly supervised approach for Indian Legal Document Summarization—0
Evidence to Generate (E2G): A Single-agent Two-step Prompting for Context Grounded and Retrieval Augmented Reasoning—0
Classifying magnetic resonance image modalities with convolutional neural networks—0
Layer-wise Investigation of Large-Scale Self-Supervised Music Representation Models—0
A Review of Speaker Diarization: Recent Advances with Deep Learning—0
EVIDENCEMINER: Textual Evidence Discovery for Life Sciences—0
LayoutCoT: Unleashing the Deep Reasoning Potential of Large Language Models for Layout Generation—0
Evidence-Driven Retrieval Augmented Response Generation for Online Misinformation—0
Classification Tree Diagrams in Health Informatics Applications—0
Evidence Contextualization and Counterfactual Attribution for Conversational QA over Heterogeneous Data with RAG Systems—0
Evidence-based Trustworthiness—0
Classification or Generation? Understanding Paradigm Shift for Knowledge-Intensive Tasks—0
A Review of Repository Level Prompting for LLMs—0
LDOP: Local Directional Order Pattern for Robust Face Retrieval—0
AGRaME: Any-Granularity Ranking with Multi-Vector Embeddings—0
EveTAR: Building a Large-Scale Multi-Task Test Collection over Arabic Tweets—0
Everything Can Be Described in Words: A Simple Unified Multi-Modal Framework with Semantic and Temporal Alignment—0
LEADRE: Multi-Faceted Knowledge Enhanced LLM Empowered Display Advertisement Recommender System—0
LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models—0
Classification on Sentence Embeddings for Legal Assistance—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