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 8201–8225 of 14297 papers

TitleStatusHype
TABi: Type-Aware Bi-encoders for End-to-End Entity Retrieval—0
Image Retrieval from Contextual Descriptions—0
Augmenting Memory Networks for Rich and Efficient Retrieval in Grounded DialogueCode0
Towards Robust Passage Re-Ranking Model by Mitigating Lexical Match Bias—0
Augmenting Document Representations for Dense Retrieval with Interpolation and Perturbation—0
Hyperlink-induced Pre-training for Passage Retrieval of Open-domain Question Answering—0
Meet Your Favorite Character: Open-domain Chatbot Mimicking Fictional Characters with only a Few Utterances—0
Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations—0
Slot Filling for Biomedical Information Extraction—0
ViQuAE, a Dataset for Knowledge-based Visual Question Answering about Named EntitiesCode0
Two-Level Supervised Contrastive Learning for Response Selection in Multi-Turn Dialogue—0
Compressing Sentence Representation via Homomorphic Projective Distillation—0
Combining static and contextualised multilingual embeddings—0
Retrieval-based Layer-wise Adaptive Transformer for Source Code Summarization—0
XLTime: A Cross-Lingual Knowledge Transfer Framework for Zero-Shot Low-Resource Language Temporal Expression Extraction—0
Entity-Conditioned Question Generation for Robust Attention Distribution in Neural Information Retrieval—0
Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data—0
Multi-Stage Prompting for Knowledgeable Dialogue Generation—0
There’s a Time and Place for Reasoning Beyond the Image—0
DialogConv: A Lightweight Fully Convolutional Network for Multi-view Response Selection—0
Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?—0
OneAligner: Zero-shot Cross-lingual Transfer with One Rich-Resource Language Pair for Low-Resource Sentence Retrieval—0
Block-Sparse Recovery Network for Two-Dimensional Harmonic Retrieval—0
LiT: Zero-Shot Transfer with Locked-image text TuningCode1
Full-attention based Neural Architecture Search using Context Auto-regression—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