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 3851–3875 of 14297 papers

TitleStatusHype
Background Denoising for Ptychography via Wigner Distribution Deconvolution—0
Fine-tuning CLIP Text Encoders with Two-step Paraphrasing—0
ColBERT-XM: A Modular Multi-Vector Representation Model for Zero-Shot Multilingual Information RetrievalCode1
Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Models—0
INSTRUCTIR: A Benchmark for Instruction Following of Information Retrieval ModelsCode1
Assessing generalization capability of text ranking models in Polish—0
CLoVe: Encoding Compositional Language in Contrastive Vision-Language ModelsCode1
UFO: a Unified and Flexible Framework for Evaluating Factuality of Large Language ModelsCode0
2D Matryoshka Sentence EmbeddingsCode4
Federated Neural Graph Databases—0
Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility GenerationCode2
Tug-of-War Between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-Augmented Language Models—0
OpenTab: Advancing Large Language Models as Open-domain Table ReasonersCode1
Assisting in Writing Wikipedia-like Articles From Scratch with Large Language ModelsCode14
From Keywords to Structured Summaries: Streamlining Scholarly Information Access—0
LLM Based Multi-Agent Generation of Semi-structured Documents from Semantic Templates in the Public Administration DomainCode1
Retrieval-Augmented Data Augmentation for Low-Resource Domain Tasks—0
Sparse and Structured Hopfield NetworksCode0
BIRCO: A Benchmark of Information Retrieval Tasks with Complex ObjectivesCode0
MORE: Multi-mOdal REtrieval Augmented Generative Commonsense Reasoning—0
Self-DC: When to Reason and When to Act? Self Divide-and-Conquer for Compositional Unknown Questions—0
Whispers in Grammars: Injecting Covert Backdoors to Compromise Dense Retrieval SystemsCode0
Analysing The Impact of Sequence Composition on Language Model Pre-TrainingCode1
Leveraging Translation For Optimal Recall: Tailoring LLM Personalization With User Profiles—0
ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented AgentsCode2
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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