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 7826–7850 of 14297 papers

TitleStatusHype
Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions—0
Live Laparoscopic Video Retrieval with Compressed Uncertainty—0
LiveVLM: Efficient Online Video Understanding via Streaming-Oriented KV Cache and Retrieval—0
Living Lab Evaluation for Life and Social Sciences Search Platforms -- LiLAS at CLEF 2021—0
Living Labs - An Ethical Challenge for Researchers and Platform Providers—0
Evaluating Machine Translation in Cross-lingual E-Commerce Search—0
Llama Nemoretriever Colembed: Top-Performing Text-Image Retrieval Model—0
MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders are Better Dense Retrievers—0
MatAtlas: Text-driven Consistent Geometry Texturing and Material Assignment—0
Evaluating Long Range Dependency Handling in Code Generation Models using Multi-Step Key Retrieval—0
Evaluating Local Geometric Feature Representations for 3D Rigid Data Matching—0
Chunk Twice, Embed Once: A Systematic Study of Segmentation and Representation Trade-offs in Chemistry-Aware Retrieval-Augmented Generation—0
LLaVE: Large Language and Vision Embedding Models with Hardness-Weighted Contrastive Learning—0
Evaluating LLMs on Document-Based QA: Exact Answer Selection and Numerical Extraction using Cogtale dataset—0
Evaluating Lexical Similarity to build Sentiment Similarity—0
ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systems—0
LLM4DESIGN: An Automated Multi-Modal System for Architectural and Environmental Design—0
DiffuSyn Bench: Evaluating Vision-Language Models on Real-World Complexities with Diffusion-Generated Synthetic Benchmarks—0
LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning—0
Bisecting K-Means in RAG for Enhancing Question-Answering Tasks Performance in Telecommunications—0
LLM Agents Improve Semantic Code Search—0
Chunk-Distilled Language Modeling—0
LLM as an Art Director (LaDi): Using LLMs to improve Text-to-Media Generators—0
LLM-Assisted Proactive Threat Intelligence for Automated Reasoning—0
3D-TexSeg: Unsupervised Segmentation of 3D Texture using Mutual Transformer Learning—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