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 13201–13250 of 14297 papers

TitleStatusHype
Parameter Efficient Tuning Allows Scalable Personalization of LLMs for Text Entry: A Case Study on Abbreviation Expansion—0
Parameter-Efficient Sparse Retrievers and Rerankers using Adapters—0
Zero-Shot Interactive Text-to-Image Retrieval via Diffusion-Augmented Representations—0
Whole Page Unbiased Learning to Rank—0
Training-Free Personalization via Retrieval and Reasoning on Fingerprints—0
Training-free Zero-shot Composed Image Retrieval with Local Concept Reranking—0
Zero-Shot Learning Based Approach For Medieval Word Recognition Using Deep-Learned Features—0
Training neural networks with structured noise improves classification and generalization—0
WHOSE - A Tool for Whole-Session Analysis in IIR—0
Parallel Structure from Motion for UAV Images via Weighted Connected Dominating Set—0
Training Plug-n-Play Knowledge Modules with Deep Context Distillation—0
Training Semantic Descriptors for Image-Based Localization—0
Training Task Experts through Retrieval Based Distillation—0
Zero-Shot Learning by Generating Pseudo Feature Representations—0
Parallel Sentence Retrieval From Comparable Corpora for Biomedical Text Simplification—0
Train/Test-Time Adaptation with Retrieval—0
Who to Watch Next: Two-side Interactive Networks for Live Broadcast Recommendation—0
Parallel Key-Value Cache Fusion for Position Invariant RAG—0
PaRaDe: Passage Ranking using Demonstrations with Large Language Models—0
Transcending Language Boundaries: Harnessing LLMs for Low-Resource Language Translation—0
TransCompressor: LLM-Powered Multimodal Data Compression for Smart Transportation—0
Transcribing Lyrics From Commercial Song Audio: The First Step Towards Singing Content Processing—0
Transcription and translation of videos using fine-tuned XLSR Wav2Vec2 on custom dataset and mBART—0
Transcription-Enriched Joint Embeddings for Spoken Descriptions of Images and Videos—0
Why are Sequence-to-Sequence Models So Dull? Understanding the Low-Diversity Problem of Chatbots—0
Transcript to Video: Efficient Clip Sequencing from Texts—0
ParaCNN: Visual Paragraph Generation via Adversarial Twin Contextual CNNs—0
Transductive Optimization of Top k Precision—0
Why distillation helps: a statistical perspective—0
Transfer Adversarial Hashing for Hamming Space Retrieval—0
PAQA: Toward ProActive Open-Retrieval Question Answering—0
Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models—0
PaperQA: Retrieval-Augmented Generative Agent for Scientific Research—0
Transfer Learning with Convolutional Networks for Atmospheric Parameter Retrieval—0
Transfer of knowledge among instruments in automatic music transcription—0
Paper evolution graph: Multi-view structural retrieval for academic literature—0
Transformation of XML Documents with Prolog—0
Paper Copilot: A Self-Evolving and Efficient LLM System for Personalized Academic Assistance—0
Transformer-based Clipped Contrastive Quantization Learning for Unsupervised Image Retrieval—0
Transformer-based Cross-Modal Recipe Embeddings with Large Batch Training—0
Transformer Based Language Models for Similar Text Retrieval and Ranking—0
Panorama to panorama matching for location recognition—0
Transformer-empowered Multi-modal Item Embedding for Enhanced Image Search in E-Commerce—0
Pan More Gold from the Sand: Refining Open-domain Dialogue Training with Noisy Self-Retrieval Generation—0
PanguIR Technical Report for NTCIR-18 AEOLLM Task—0
Transformers and CNNs both Beat Humans on SBIR—0
Why does PairDiff work? - A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection—0
Transforming LLMs into Cross-modal and Cross-lingual Retrieval Systems—0
Transforming Location Retrieval at Airbnb: A Journey from Heuristics to Reinforcement Learning—0
Transforming Wikipedia into an Ontology-based Information Retrieval Search Engine for Local Experts using a Third-Party Taxonomy—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