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 5601–5650 of 14297 papers

TitleStatusHype
GenQREnsemble: Zero-Shot LLM Ensemble Prompting for Generative Query Reformulation—0
MatAtlas: Text-driven Consistent Geometry Texturing and Material Assignment—0
uTeBC-NLP at SemEval-2024 Task 9: Can LLMs be Lateral Thinkers?Code0
A Coupled Neural Field Model for the Standard Consolidation Theory—0
Enhancing Cross-lingual Sentence Embedding for Low-resource Languages with Word Alignment—0
Retrieving Examples from Memory for Retrieval Augmented Neural Machine Translation: A Systematic Comparison—0
Dynamic Demonstration Retrieval and Cognitive Understanding for Emotional Support ConversationCode0
Dissecting Paraphrases: The Impact of Prompt Syntax and supplementary Information on Knowledge Retrieval from Pretrained Language Models—0
FraGNNet: A Deep Probabilistic Model for Mass Spectrum Prediction—0
Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized ContextsCode0
Entity Disambiguation via Fusion Entity Decoding—0
A Comprehensive Survey on AI-based Methods for Patents—0
Transforming LLMs into Cross-modal and Cross-lingual Retrieval Systems—0
A Novel Audio Representation for Music Genre Identification in MIR—0
BERT-Enhanced Retrieval Tool for Homework Plagiarism Detection System—0
Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval—0
Exploring the Nexus of Large Language Models and Legal Systems: A Short Survey—0
On Train-Test Class Overlap and Detection for Image RetrievalCode0
Observations on Building RAG Systems for Technical Documents—0
Query-driven Relevant Paragraph Extraction from Legal Judgments—0
RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation—0
CuSINeS: Curriculum-driven Structure Induced Negative Sampling for Statutory Article Retrieval—0
On the Estimation of Image-matching Uncertainty in Visual Place Recognition—0
ECtHR-PCR: A Dataset for Precedent Understanding and Prior Case Retrieval in the European Court of Human RightsCode0
Do Vision-Language Models Understand Compound Nouns?Code0
Planning and Editing What You Retrieve for Enhanced Tool LearningCode0
Aligning Large Language Models with Recommendation Knowledge—0
Multi-hop Question Answering under Temporal Knowledge Editing—0
Shallow Cross-Encoders for Low-Latency RetrievalCode0
The Future of Combating Rumors? Retrieval, Discrimination, and Generation—0
Gecko: Versatile Text Embeddings Distilled from Large Language Models—0
Towards a Robust Retrieval-Based Summarization SystemCode0
FairRAG: Fair Human Generation via Fair Retrieval Augmentation—0
Generating Multi-Aspect Queries for Conversational Search—0
PointCloud-Text Matching: Benchmark Datasets and a Baseline—0
Are Large Language Models Good at Utility Judgments?Code0
Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check—0
Improving Attributed Text Generation of Large Language Models via Preference Learning—0
DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment—0
Decoy Effect In Search Interaction: Understanding User Behavior and Measuring System Vulnerability—0
CPR: Retrieval Augmented Generation for Copyright Protection—0
RAP: Retrieval-Augmented Planner for Adaptive Procedure Planning in Instructional Videos—0
Online Embedding Multi-Scale CLIP Features into 3D Maps—0
Leveraging Large Language Models for Relevance Judgments in Legal Case Retrieval—0
High Recall, Small Data: The Challenges of Within-System Evaluation in a Live Legal Search System—0
AIR-HLoc: Adaptive Retrieved Images Selection for Efficient Visual Localisation—0
Evaluation of Semantic Search and its Role in Retrieved-Augmented-Generation (RAG) for Arabic Language—0
LLMs in HCI Data Work: Bridging the Gap Between Information Retrieval and Responsible Research Practices—0
Scaling Laws For Dense RetrievalCode0
Text Is MASS: Modeling as Stochastic Embedding for Text-Video Retrieval—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